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   "source": [
    "# Debiasing with Orthogonalization\n",
    "\n",
    "Previously, we saw how to evaluate a causal model. By itself, that's a huge deed. Causal models estimates the elasticity $\\frac{\\delta y}{\\delta t}$, which is an unseen quantity. Hence, since we can't see the ground truth of what our model is estimating, we had to be very creative in how we would go about evaluating them. \n",
    "\n",
    "The technique shown on the previous chapter relied heavily on data where the treatment was randomly assigned. The idea was to estimate the elasticity $\\frac{\\delta y}{\\delta t}$ as the coefficient of a single variable linear regression of `y ~ t`. However, this only works if the treatment is randomly assigned. If it isn't, we get into trouble due to omitted variable bias. \n",
    "\n",
    "To workaround this, we need to make the data look as if the treatment is randomly assigned. I would say there are two main techniques to do this. One is using propensity score and the other using orthogonalization. We will cover the latter in this chapter.\n",
    "\n",
    "One final word of caution before we continue. I would argue that probably the safest way out of non random data is to go out and do some sort of experiment to gather random data. I myself don't trust very much on debiasing techniques because you can never know if you've accounted for every confounder. Having said that, orthogonalization is still very much worth learning. It's an incredibly powerful technique that will be the foundation of many causal models to come. \n",
    "\n",
    "## Linear Regression Reborn\n",
    "\n",
    "The idea of orthogonalization is based on a theorem designed by three econometricians in 1933, Ragnar Frisch, Frederick V. Waugh, and Michael C. Lovell. Simply put, it states that you can decompose any multivariable linear regression model into three stages or models. Let's say that your features are in an $X$ matrix. Now, you partition that matrix in such a way that you get one part, $X_1$, with some of the features and another part, $X_2$, with the rest of the features. \n",
    "\n",
    "In the first stage, we take the first set of features and estimate the following linear regression model \n",
    "\n",
    "$$\n",
    "y_i = \\theta_0 + \\pmb{\\theta_1 X}_{1i} + e_i\n",
    "$$\n",
    "\n",
    "where $\\pmb{\\theta_1}$ is a vector of parameters. We then take the residuals of that model\n",
    "\n",
    "$$\n",
    "y^* = y_i - (\\hat{\\theta}_0 + \\pmb{\\hat{\\theta}_1 X}_{1i})\n",
    "$$\n",
    "\n",
    "On the second stage, we take the first set of features again, but now we run a model where we estimate the second set of features\n",
    "\n",
    "$$\n",
    "\\pmb{X}_{2i} = \\gamma_0 + \\pmb{\\gamma_1 X}_{1i} + e_i\n",
    "$$\n",
    "\n",
    "Here, we are using the first set of features to predict the second set of features. Finally, we also take the residuals for this second stage.\n",
    "\n",
    "$$\n",
    "\\pmb{X}^*_{2i} = \\pmb{X}_{2i} - (\\hat{\\gamma}_0 + \\pmb{\\hat{\\gamma}_1 X}_{1i})\n",
    "$$\n",
    "\n",
    "\n",
    "Lastly, we take the residuals from the first and second stage, and estimate the following model\n",
    "\n",
    "$$\n",
    "y_i^* = \\beta_0 + \\pmb{\\beta_2 X}^*_{2i} + e_i\n",
    "$$\n",
    "\n",
    "The Frisch–Waugh–Lovell theorem states that the parameter estimate $\\pmb{\\hat{\\beta}_2}$ from estimating this model is equivalent to the one we get by running the full regression, with all the features:\n",
    "\n",
    "$$\n",
    "y_i = \\beta_0 + \\pmb{\\beta_1 X}_{1i} + \\pmb{\\beta_2 X}_{2i} + e_i\n",
    "$$\n",
    "\n",
    "![img](./data/img/orthogonal/nazare-confusa.jpg)\n",
    "\n",
    "OK. Let's unpack this a bit further. We know that regression is a very special model. Each of its parameters has the interpretation of a partial derivative: how much would $Y$ increase if I increase one feature **while holding all the others fixed**. This is very nice for causal inference, because it means we can control for variables in the analysis, even if those same variables have not been held fixed during the collection of the data.\n",
    "\n",
    "We also know that if we omit variables from the regression, we get bias. Specifically, omitted variable bias (or confounding bias). Still, the Frisch–Waugh–Lovell is saying that I can break my regression model into two parts, neither of them containing the full feature set, and still get the same estimate I would get by running the entire regression. Not only that, this theorem also provides some insight into what linear regression is doing. To get the coefficient of one variable $X_k$, regression first uses all the other variables to predict $X_k$ and takes the residuals. This \"cleans\"  $X_k$ of any influence from those variables. That way, when we try to understand $X_k$'s impact on $Y$, it will be free from omitted variable bias. Second, regression uses all the other variables to predict $Y$ and takes the residuals. This \"cleans\" $Y$ from any influence from those variables, reducing the variance of $Y$ so that it is easier to see how $X_k$ impacts $Y$. \n",
    "\n",
    "I know it can be hard to appreciate how awesome this is. But remember what linear regression is doing. It's estimating the impact of $X_2$ on $y$ while accounting for $X_1$. This is incredibly powerful for causal inference. It says that I can build a model that predicts my treatment $t$ using my features $X$, a model that predicts the outcome $y$ using the same features, take the residuals from both models and run a model that estimates how the residual of $t$ affects the residual of $y$. This last model will tell me how $t$ affects $y$ while controlling for $X$. In other words, the first two models are controlling for the confounding variables. They are generating data which is as good as random. This is debiasing my data. That's what we use in the final model to estimate the elasticity.\n",
    "\n",
    "There is a (not so complicated) mathematical proof for why that is the case, but I think the intuition behind this theorem is so straightforward we can go directly into it.\n",
    "\n",
    "## The Intuition Behind Orthogonalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from nb21 import cumulative_elast_curve_ci, elast, cumulative_gain_ci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take our price data once again. But now, we will only take the sample where prices where **not** randomly assigned. Once again, we separate them into a training and a test set. Since we will use the test set to evaluate our causal model, let's see how we can use orthogonalization to debias it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5000, 5), (5000, 5))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = pd.read_csv(\"./data/ice_cream_sales.csv\")\n",
    "\n",
    "train, test = train_test_split(prices, test_size=0.5)\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we show the correlations on the test set, we can see that price is positively correlated with sales, meaning that sales should go up as we increase prices. This is obviously nonsense. People don't buy more if ice cream is expensive. We probably have some sort of bias here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>weekday</th>\n",
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       "      <th>price</th>\n",
       "      <th>sales</th>\n",
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       "  </thead>\n",
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       "      <th>temp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003630</td>\n",
       "      <td>0.006605</td>\n",
       "      <td>-0.011977</td>\n",
       "      <td>0.379108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>0.003630</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011889</td>\n",
       "      <td>0.002610</td>\n",
       "      <td>0.004589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cost</th>\n",
       "      <td>0.006605</td>\n",
       "      <td>0.011889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.388046</td>\n",
       "      <td>-0.009410</td>\n",
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       "      <th>price</th>\n",
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       "      <td>0.002610</td>\n",
       "      <td>0.388046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.379108</td>\n",
       "      <td>0.004589</td>\n",
       "      <td>-0.009410</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             temp   weekday      cost     price     sales\n",
       "temp     1.000000  0.003630  0.006605 -0.011977  0.379108\n",
       "weekday  0.003630  1.000000  0.011889  0.002610  0.004589\n",
       "cost     0.006605  0.011889  1.000000  0.388046 -0.009410\n",
       "price   -0.011977  0.002610  0.388046  1.000000  0.080040\n",
       "sales    0.379108  0.004589 -0.009410  0.080040  1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot our data, we can see why this is happening. Weekends (Saturday and Sunday) have higher price but also higher sales. We can see that this is the case because the weekend cloud of points seems to be to the upper right part of the plot.\n",
    "\n",
    "Weekend is probably playing an important role in the bias here. On the weekends, there are more ice cream sales because there is more demand. In response to that demand, prices go up. So it is not that the increase in price causes sales to go up. It is just that both sales and prices are high on weekends. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "sns.scatterplot(data=test.sample(1000), x=\"price\", y=\"sales\", hue=\"weekday\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To debias this dataset we will need two models. The first model, let's call it $M_t(X)$, predicts the treatment (price, in our case) using the confounders. It's the one of the stages we've seen above, on the Frisch–Waugh–Lovell theorem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_t = smf.ols(\"price ~ cost + C(weekday) + temp\", data=test).fit()\n",
    "debiased_test = test.assign(**{\"price-Mt(X)\":test[\"price\"] - m_t.predict(test)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have this model, we will construct the residuals\n",
    "\n",
    "$$\n",
    "\\hat{t}_i = t_i - M_t(X_i)\n",
    "$$\n",
    "\n",
    "You can think of this residual as a version of the treatment that is unbiased or, better yet, that is impossible to predict from the confounders $X$. Since the confounders were already used to predict $t$, the residual is by definition, unpredictable with $X$. Another way of saying this is that the bias has been explained away by the model $M_t(X_i)$, producing $\\hat{t}_i$ which is as good as randomly assigned. Of course this only works if we have in $X$ all the confounders that cause both $T$ and $Y$.\n",
    "\n",
    "We can also plot this data to see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "sns.scatterplot(data=debiased_test.sample(1000), x=\"price-Mt(X)\", y=\"sales\", hue=\"weekday\")\n",
    "plt.vlines(0, debiased_test[\"sales\"].min(), debiased_test[\"sales\"].max(), linestyles='--', color=\"black\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the weekends are no longer to the upper right corner. They got pushed to the center. Moreover, we can no longer differentiate between different price levels (the treatment) using the weekdays. We can say that the residual $price-M_t(X)$, plotted on the x-axis, is a \"random\" or debiased version of the original treatment.\n",
    "\n",
    "This alone is sufficient to debias the dataset. This new treatment we've created is as good as randomly assigned. But we can still do one other thing to make the debiased dataset even better. Namely, we can also construct residuals for the outcome.\n",
    "\n",
    "$$\n",
    "\\hat{y}_i = y_i - M_y(X_i)\n",
    "$$\n",
    "\n",
    "This is another stage from the Frisch–Waugh–Lovell theorem. It doesn't make the set less biased, but it makes it easier to estimate the elasticity by reducing the variance in $y$. Once again, you can think about $\\hat{y}_i$ as a version of $y_i$ that is unpredictable from $X$ or that had all its variances due to $X$ explained away. Think about it. We've already used $X$ to predict $y$ with $M_y(X_i)$. And $\\hat{y}_i$ is the error of this prediction. So, by definition, it's not possible to predict it from $X$. All the information in $X$ to predict $y$ has already been used. If that is the case, the only thing left to explain $\\hat{y}_i$ is something we didn't used to construct it (not included in $X$), which is only the treatment (again, assuming no unmeasured confounders). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_y = smf.ols(\"sales ~ cost + C(weekday) + temp\", data=test).fit()\n",
    "\n",
    "debiased_test = test.assign(**{\"price-Mt(X)\":test[\"price\"] - m_t.predict(test),\n",
    "                               \"sales-My(X)\":test[\"sales\"] - m_y.predict(test)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we do both transformations, not only does weekdays not predict the price residuals, but it also can't predict the residual of sales $\\hat{y}$. The only thing left to predict these residuals is the treatment. Also, notice something interesting. In the plot above, it was hard to know the direction of the price elasticity. It looked like sales decreased as prices went up, but there was such a large variance in sales that it was hard to say that for sure. \n",
    "\n",
    "Now, when we plot the two residuals, it becomes much clear that sales indeed causes prices to go down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "sns.scatterplot(data=debiased_test.sample(1000), x=\"price-Mt(X)\", y=\"sales-My(X)\", hue=\"weekday\")\n",
    "plt.vlines(0, debiased_test[\"sales-My(X)\"].min(), debiased_test[\"sales-My(X)\"].max(), linestyles='--', color=\"black\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One small disadvantage of this debiased data is that the residuals have been shifted to a different scale. As a result, it's hard to interpret what they mean (what is a price residual of -3?). Still, I think this is a small price to pay for the convenience of building random data from data that was not initially random. \n",
    "\n",
    "To summarize, by predicting the treatment, we've constructed $\\hat{t}$ which works as an unbiased version of the treatment; by predicting the outcome, we've constructed $\\hat{y}$ which is a version of the outcome that can only be further explained if we use the treatment. This data, where we replace $y$ by $\\hat{y}$ and $t$ by $\\hat{t}$ is the debiased data we wanted. We can use it to evaluate our causal model just like we deed previously using random data.\n",
    "\n",
    "To see this, let's once again build a causal model for price elasticity using the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "m3 = smf.ols(f\"sales ~ price*cost + price*C(weekday) + price*temp\", data=train).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll make elasticity predictions on the debiased test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>weekday</th>\n",
       "      <th>cost</th>\n",
       "      <th>price</th>\n",
       "      <th>sales</th>\n",
       "      <th>price-Mt(X)</th>\n",
       "      <th>sales-My(X)</th>\n",
       "      <th>m3_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7791</th>\n",
       "      <td>20.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>187</td>\n",
       "      <td>-0.201769</td>\n",
       "      <td>1.441373</td>\n",
       "      <td>-0.073317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>26.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>201</td>\n",
       "      <td>-0.179506</td>\n",
       "      <td>4.737748</td>\n",
       "      <td>-2.139611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5785</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>186</td>\n",
       "      <td>-0.215107</td>\n",
       "      <td>-5.855171</td>\n",
       "      <td>-0.549798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542</th>\n",
       "      <td>20.9</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>180</td>\n",
       "      <td>-1.401386</td>\n",
       "      <td>-5.743172</td>\n",
       "      <td>-0.108943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9250</th>\n",
       "      <td>26.7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>201</td>\n",
       "      <td>0.978382</td>\n",
       "      <td>4.384885</td>\n",
       "      <td>-1.427230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      temp  weekday  cost  price  sales  price-Mt(X)  sales-My(X)   m3_pred\n",
       "7791  20.8        3   1.5    6.3    187    -0.201769     1.441373 -0.073317\n",
       "1764  26.6        3   1.5    6.3    201    -0.179506     4.737748 -2.139611\n",
       "5785  24.0        4   1.0    5.8    186    -0.215107    -5.855171 -0.549798\n",
       "3542  20.9        3   1.5    5.1    180    -1.401386    -5.743172 -0.108943\n",
       "9250  26.7        5   1.0    7.0    201     0.978382     4.384885 -1.427230"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_elast(model, price_df, h=0.01):\n",
    "    return (model.predict(price_df.assign(price=price_df[\"price\"]+h))\n",
    "            - model.predict(price_df)) / h\n",
    "\n",
    "debiased_test_pred = debiased_test.assign(**{\n",
    "    \"m3_pred\": predict_elast(m3, debiased_test),\n",
    "})\n",
    "\n",
    "debiased_test_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, when it comes to plotting the cumulative elasticity, we still order the dataset by the predictive elasticity, but now we use the debiased versions of the treatment and outcome to get this elasticity. This is equivalent to estimating $\\beta_1$ in the following regression model\n",
    "\n",
    "$$\n",
    "\\hat{y}_i = \\beta_0 + \\beta_1 \\hat{t}_i + e_i\n",
    "$$\n",
    "\n",
    "where the residuals are like we've described before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "cumm_elast = cumulative_elast_curve_ci(debiased_test_pred, \"m3_pred\", \"sales-My(X)\", \"price-Mt(X)\", min_periods=50, steps=200)\n",
    "x = np.array(range(len(cumm_elast)))\n",
    "plt.plot(x/x.max(), cumm_elast, color=\"C0\")\n",
    "\n",
    "plt.hlines(elast(debiased_test_pred, \"sales-My(X)\", \"price-Mt(X)\"), 0, 1, linestyles=\"--\", color=\"black\", label=\"Avg. Elast.\")\n",
    "plt.xlabel(\"% of Top Elast. Customers\")\n",
    "plt.ylabel(\"Elasticity of Top %\")\n",
    "plt.title(\"Cumulative Elasticity\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same thing for the cumulative gain curve, of course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "cumm_gain = cumulative_gain_ci(debiased_test_pred, \"m3_pred\", \"sales-My(X)\", \"price-Mt(X)\", min_periods=50, steps=200)\n",
    "x = np.array(range(len(cumm_gain)))\n",
    "plt.plot(x/x.max(), cumm_gain, color=\"C1\")\n",
    "\n",
    "plt.plot([0, 1], [0, elast(debiased_test_pred, \"sales-My(X)\", \"price-Mt(X)\")], linestyle=\"--\", label=\"Random Model\", color=\"black\")\n",
    "\n",
    "plt.xlabel(\"% of Top Elast. Customers\")\n",
    "plt.ylabel(\"Cumulative Gain\")\n",
    "plt.title(\"Cumulative Gain on Debiased Sample\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how similar these plots are to the ones in the previous chapter. This is some indication that the debiasing worked wonders here. \n",
    "\n",
    "In contrast, let's see what the cumulative gain plot would look like if we used the original, biased data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G1w1av359Zs+efUnnERGRXOL4ARjRH7bNgQFjoEa41xFlOc/GpBljKhpjphhjVhljVhpj/pXKMcYY854xZr0xZpkx5nIvYs1I+fPn57333uPtt98mISGBw4cPU758ecCNQzufFi1aMG3aNPbv3098fDyjR49O3te6devkMWTfffcdbdu2veR427ZtyxNPPEH//v3/sX3kyJEkJiayd+9epk+fTvPmzWnXrl3y9p07dzJlyhQAateuzd69e5OTtPj4eFauXHnJ8YmIiMeshcT48x93IQ5ugWFdYcciKFQBfr4fThzK2GtkA162pCUA/7HWLjLGFAQWGmMirbWrUhzTHajpu7UAPvZ9zdaaNGlCo0aNGDFiBI8++igDBw7k5Zdf5qqrrjrvc8uWLcvzzz9Pq1atKFKkyBndlO+//z6DBw/mzTffpGTJknz55ZeXHKsxhocffvgf23v37s3s2bO57LLLMMbwxhtvUKZMGXr37s3kyZOpV68elSpVolWrVgDkyZOHH3/8kQceeIDDhw+TkJDAgw8+SP369S85RhER8cDOZbDiR1j1MxzZCY37Q+sHoHj18z83MQEObISTMb7bMXc7uBm2zIStcyAwGG4eB8Eh8HkETHgCen+c2a/Kr5iUdbC8ZIz5GfjAWhuZYtunwFRr7Qjf4zVAB2vtOacFhoWF2QULFpyxbfXq1dStWzdzApdMpfdORMQPJMRBkG+oTFIiTH0Npr8FAYFQrQMUKAPLR0PiSej2GrS8+9znOrwdvr8Bdi9PfX+p+lDlCmg+BErUdNsmvwzT34Q+n0Gj6zP0pXnNGLPQWhuW2j6/GJNmjKkCNAHmnrWrPLAtxeNo37YzkjRjzBBgCEClSpUyLU4REZFcZ/siGNYNilSC2t1gxxLY/Bc0GQARL0H+Yu64zs/CT0NgyqvQ6IbT21PatQK+uw7ijsJVb7uuzDyhvlsBCC2R+vPaPQrrJ8HYO2DFWOj6Svpa7LI5z+ukGWMKAGOAB621Ry7mHNbaodbaMGtt2KlZjyIiInKJEk7Cz/dBvqJQuALM+QSiF0Cvj6Hnh2cmVAVLQ5dXIO4IzPnon+faPNMlewC3/gHNbndJX9W2UP5yN4MztQQNICgP3DoBwp93CeJHLWF5zl+pxtOWNGNMMC5B+85aOzaVQ7YDFVM8ruDbJiIiIhnBWvjjUTdYv1JLqHwFVGgGAQEw43+wZyX0H+kSqtgjgIWQwqmfq0wDqNfTJXMt7zmddG2a7ro4C1dw48wKl7/wOIPyQpt/w2X94cdbYcztcHw/tLjzYl+53/NydqcBvgBWW2vfOcdh44FbfLM8WwKH0xqPlhZ/GXsn6af3TEQkC6z+BeYNhV3LYdILMKwLfBDmui2nvwkNr3MJGkBIoXMnaKe0f9xNBpj9IRzbD0tGwHfXQ5HKMOi3i0vQUipYBgaMhTpXueRy4jOnZ5eePA5j7oD3Lof5n0N87KVdy2OeTRwwxrQB/gKWA0m+zU8ClQCstZ/4ErkPgG7AcWCwtXZBKqdLltrEgU2bNlGwYEGKFy+OO6X4O2st+/fv5+jRo1StWtXrcEREco4ts6FoFShU1o0N+6A55C8OQ6ZC7GFYHwULhrn6ZPlLwL3zILT4hV1j9CA369P6/r2Xbgi3jHNjzjJKYoJL0hZ8AeWbQsSL8OdTsHMplKrnWgALlIbrvobKrTLuuhksrYkDfjO7M6OklqTFx8cTHR1NbGz2zqhzm5CQECpUqEBwcLDXoYiIZH8JJ2Hi0zDvU8hbGLq9CrtXufFjt0VCxWZnHr97letivJgB+gc2uZa4UnVdF2r5MDeuLDOsHAe//AtiD7nJB9d+AbW6urFrP98LwaFw90w3E9UP5fokTUREJFc7uhtG3Qzb5roB+3tWu3pkAE0HQ4//8zS8S3Z4u+tevfxmlxiesnIcjB4I13zg9vkhvy/BISIiIpnoj0dd8dm+X0KDPpCUBPM/g3UTIfw5r6O7dIXLu5bBs9Xr6VrxprwKDa6FPPmzPrZL4HkJDhEREclE+9a58WGt7nEJGriZmy3udGti5ivqbXyZyRg3Vu3oDpj7idfRXDAlaSIiIjnZjP+DoBBokcYqADlZlSugVnf4621Xqy0bUZImIiKSUx3aCst+gKYDoUAuLvZ+1VtQqBx82ytbFcFVkiYiIpJTzXofMND6fq8j8VbhCnDrn2582pjbYPFwryNKFyVpIiIi2Z21sC4Kts6FmD1u6aaf74MFX8Jl/VySktvlLwY3/wRV2sKfT8LxA15HdF6a3SkiIpLdbZgM31175rbg/HDZDRD+gjcx+aPgEOj+BnxyBUz7L3T/r9cRpUlJmoiISHa36ufThVwPbXWlJupe45ZxkjOVrgeXD3TLRjW7HUrU9Dqic1KSJiIikp0lJcLfv7kq+6fW2JS0dXzKTSD45V9QpiFs+st9//ysZpzGpImIiGRnW2fD8X2u5UzSp0BJaPewW3Vh4dduSam5n0BcjNeRnUFJmoiISHa2aryrg1Yj3OtIspfWD8Cdf8HjW+DazyH+OKz53euozqAkTUREJLtKSoLVv7gELW8Br6PJXgICoGwjt4h8xZZQuCIsG+V1VGdQkiYiIpJd7VjkljxSV+elCQiAhte5WbIxe72OJpmSNBERkexq1c8QEOwGvculaXQ92ERYOdbrSJIpSRMREcmOYvbC4m+hRmfIV8TraLK/UnXdTM9lI72OJJmSNBERkezoj0fh5DGIeMnrSHKOhtfD9oVuMfbD272ORkmaiIhItrPmD9ct1+5RKFnL62hyjiYDoFJrmPQi/K8+DO/rltzyiIrZioiIZCexR+C3/0CpenDFv7yOJmfJXwxu/QP2b4Dlo9332hjPwlGSJiIikp3M/gCObIfrv4GgPF5HkzMVrw4dHvc6CnV3ioiIZBsnDsKcj13JjQphXkcjmUxJmoiISHYx52OIOwLtH/M6EskCStJERESyg+RWtB5QpoHX0UgWUJImIiKSHcz5RK1ouYySNBEREX93/ADM+QjqXO0KrkquoCRNRETE3834H8QdhY5PeR2JZCElaSIiIv7syA6YNxQa3QCl63kdjWQhJWkiIiL+bNp/ISkROj7hdSSSxZSkiYiI+Kv9G2DRtxA2GIpW8ToayWJK0kRERPzVlFcgKC+0e8TrSMQDStJERET80c5lsGIMtLwbCpTyOhrxgJI0ERERfzTpRQgpAq0f8DoS8YiSNBEREX+zZRasj4Q2/4Z8RbyORjyiJE1ERMSfJJyEyOegQBloPsTraMRDQV4HICIiIj6xh2HkzRA9D3p9Annyex2ReEhJmoiIiD84HA3D+8L+ddDrY2jc3+uIxGNK0kRERPzBr/92idqAMVCtg9fRiB/QmDQRERGv7VoB6yZCm38pQZNkStJERES8NvP/IE8BaHa715GIH1GSJiIi4qUDm1zR2qaDIF9Rr6MRP6IkTURExEuzPwATCK3u9ToS8TNK0kRERLxybB8sHg6X3QCFynkdjfgZJWkiIiJeWfI9JMRCS7WiyT95mqQZY4YZY/YYY1acY38HY8xhY8wS3+3ZrI5RREQkU1gLC7+Cii2hdD2voxE/5HVL2ldAt/Mc85e1trHv9mIWxCQiIpL5Nk2HAxsgbLDXkYif8jRJs9ZOBw54GYOIiIgnFn4JIUWgXk+vIxE/5XVLWnq0MsYsNcb8YYypn9oBxpghxpgFxpgFe/fuzer4RERELkzMXlj9K1zWH4LzeR2N+Cl/T9IWAZWttZcB7wPjUjvIWjvUWhtmrQ0rWbJkVsYnIiJy4ZZ8B0nxrjaayDn4dZJmrT1irY3x3f8dCDbGlPA4LBERkYtnLSz+1k0YKFXH62jEj/l1kmaMKWOMMb77zXHx7vc2KhERkUsQvQD2r4cmN3kdifi5IC8vbowZAXQAShhjooHngGAAa+0nQF/gbmNMAnAC6GettR6FKyIicumWfg9B+aBeL68jET/naZJmre1/nv0fAB9kUTgiIiKZKz7WrdNZtweEFPI6GvFzft3dKSIikqOs+R1iD0PjNNsoRAAlaSIiIlln6QgoWA6qtvc6EskGlKSJiIhkhaO7YH0UXNYPAgK9jkayASVpIiIiWWHZKLBJ0PhGryORbEJJmoiISGaz1nV1lg+DEjW9jkayCSVpIiIimW3nEtizSq1ockGUpImIiGS2JSMgMC806ON1JJKNKEkTERHJTAknYfloqN0d8hX1OhrJRpSkiYiIZKZ1E+HEAWisZaDkwihJExERyUxLvocCpaF6J68jkWxGSZqIiEhmObgZ1v4Bl/WHQE9XYpRsSEmaiIhIZpk7FEwANB/idSSSDSlJExERyQyxh2HRN1C/NxQu73U0kg7WWqy1AAwbNoz4+HhP41GSJiIikhkWfQsnj0LLe7yORNKwf/9+Ro8ezZAhQ6hWrRrLly8HoGzZsmzZssXT2NRBLiIiktESE2Dup1CpNZS/3OtoJBVr1qxhwIABLFy4EGsthQoVomPHjiQmJgLQvXt3jyNUkiYiIpLx/v4FDm+Fbq95HUmuZ61l2bJlREVFERkZSXh4OA8//DDlypUjf/78PPfcc0RERNC8eXOCgvwrLfKvaERERHKC2R9B0aqugK145o477mD8+PHs2bMHgLp165IvXz4AChYsyLRp07wM77yUpImIiGSkbfMheh50fwMCAr2OJlc4cuQIU6dOJSoqir179zJixAgATpw4QUREBBEREYSHh1O+fPaawKEkTUREJCPN+RDyFtYKA1lg5MiRvP/++8yZM4fExETy5ctHx44dSUhIICgoiOHDh3sd4iXR7E4REZGMcnALrPoZmg6EvAW8jibHsNby999/88EHH9CzZ0927NgBwIEDBzh58iSPPfYYkydP5uDBg/z2229+N7bsYuWMVyEiIuIP5g0FDLS40+tIcoQNGzbwyiuvEBkZSXR0NABVq1Zl69atlCtXjrvvvpu7777b4ygzj5I0ERGRjBB/wtVGq98LClfwOpps5/jx48yYMYPIyEhat25N7969CQ4OZty4cXTu3Jnw8HAiIiKoVq2a16FmGSVpIiIiGeHv3yDuMDQd5HUk2Ya1ljfffJM///yTmTNnEhcXR3BwMPnz56d3795UqlSJffv2ERCQO0dnKUkTERHJCEtHQOGKULmN15H4rc2bNxMVFcW+fft4/PHHMcbwww8/kJCQwL333ktERARt27YlNDQ0+Tm5NUEDJWkiIiKX7ugu2DAZ2jwEuTipSM20adP44YcfiIqKYv369QDUrl2bxx57DGMMs2bNIiQkxOMo/ZN+kkRERC7VslFgk+Cyfl5H4qmTJ08ybdo0nn76aY4dOwbA5MmTGT58OHXq1OHdd99l1apVrF69GmMMgBK0NJhTq73nFGFhYXbBggVehyEiIrmFtfBxawjOD3dM8jqaLLdr1y5GjBhBZGQk06ZN4/jx4wQGBjJ9+nRat27N0aNHCQkJITg42OtQ/ZIxZqG1Niy1fWpJExERuRS7lsOeVdC4v9eRZIkdO3bwzTffMH/+fAC2b9/OQw89xIYNGxg8eDDjxo1j//79tG7dGnDLLylBuzgakyYiInIpVo6FgCCo38frSDJFUlISf/zxB5GRkURGRrJq1SoA/v3vf9OsYW2aVMjPlkWTqdSko8eR5jxK0kRERC7Fpr+gfBjkL+Z1JBkiISGBBQsWsGfPHq655hqMMQy5/TYOHDpM27ZtGTRoEOHVQ7hs0yfw+hcEAJUAlrSBtv+G6p3BN97skh3d7dZBrXN1xp0zG1GSJiIicrHijsKOxdDm315Hckk2jXqaCbOWE7kpkcnTZnD48GEqVKhAjx49MKvHE9knhqplS5KvaRM4vtZNlCjdAFoOgULl3ezW2R/C8Gvhigch4oVLD2pdFPx0JxzfB72HwmU3XPo5sxklaSIiIhdr61ywiVAlG9VGO7CRfbFBTJk5l2uvvZaAdX/y5ptv8PGCeCoVCaRvq0ZE3PQAnbtdjTkcDePvp17DJlC8OiwY5maxtn8c2v4HgvKcPm/zIS6pmvsJtLoPCpRM/foJcTDxadcCWbQyFK0Kxaq6r3kLwIGNsG0uLPoGStVzqzdMeAyqdzr3OXMoJWkiIiIXa8sMNx6tYnOvI0lTbGwsMyN/JfLLV4mcs5TFu5KwFhbMmETT2f/iPz0a8u//PUuNnT9jVo6Ffe/A0Wow5TVISoLrv4Zi1eD4AUg8CQXL/PMiQXmg45Ow8ieY8xGEP/fPY47tg5EDYOtsqNYBDke7ZC3+2JnHBQRD2G3Q9RU4tBU+aQN/PArXfZkp3x9/pSRNRETkYm2eAeWbQp7Q8x+bhZKSkli2bBnFihWjUqVKTP3uHbrf/hTBgdCqTjlerHeY8EbluWzTp3B8P9XvGA1lLwN6QvM7YNxd8HUPd7LeQ12CBucfd1eiJtS7BuZ/Dm0ehJDCp/ftXgUjboCYPXDtF9Cwr9tuLRzbCwc2wcmj7lqFK0GgL0UpWRvaPwqTX4Z6Pd3aqLmESnCIiIhcjLgY2L7Ib7o6t23bxrBhw+jfvz9lypShSZMmDBs2DBLiaLfvO369qxYH1i9h2ortPD0sipalThC0foLrtix72ekTVW4Fd82Elve6fRc6FqzNQxB3xCVqp6z9E76IgISTMOj30wkauAkBBUpBpRZQI9wlaYFntSFd8SCUbQw/3gqzP3KJXS6gljQREZGLsW2OG49W+QpPLn/48GG2b99OvXr1iI+Pp27duhw7dowyZcrQtWtXIiIi6NKlCywYRv4T0Vz18E9QxZeMVWoJgyfA6l9Sn/SQtwB0e/XiAivX2M3wnPU+7FsPCbGuC7RsI+j/AxQqd+HnDAyGgb/AuLvhzydgxyK45gMIztmrFShJExERuRibZ/rGo7XIksvFx8czd+7c5Hpl8+bNo2HDhixevJjg4GC+/fZbatasSf369ZOXXCL2CEx/E6q2dwPvUypdz90yQ6enYOwQ1x1sE+Gy/nDV25An/8WfM6QQXP8tzHjbdX2eOAT9voOgvBkWtr9RkiYiInIxNs+Acpe7VqdMYK1l/fr11KxZE4CBAwcyYsQIAgICCAsL4/HHHyciIsIdfPwAvU+OgbgISKrtWp7AtWYd3w/hz2dKjOdUvincvzDjzxsQAO0egfwl4NcHYeTNcMO3OTZRU5ImIiJyoY4fcF1ure/P0NPu3r2bqKgooqKiiIyMZPv27WzZsoVKlSpx991307dvXzp27EjRokXPfOKyka7rcvUv8NdbbubkntWuhlv93lD+8gyN03NhgwELv/7bJWrXfw3B+dy+fetg/3qo2dUlddmYkjQREZELNf0tVy+s0aUVWD1+/DhJSUkUKFCAcePG0bt3bwCKFStG586diYiIoFChQgC0bdv23CdaMQZKN4TOz8L0N2DVeChd39Uua/PQJcXot8JudV9/fQiG94X+I+DvX93jhBNQppFrQazeKduuVqAkTURE5EIc3AzzhkLjm6BU3Qt6amJiIosWLUpuKZs5cyZvv/029913Hy1btuS1114jIiKCxo0bExgYmP54oue7hKRWF3fLLcJuhbyFXBHd95vCsT1QpS00vM61KA7vA93+Cy3v8jrSi6IkTURE5EJMftlNGOj4ZLoOP3bsGKGhoZw4cYJKlSqxb98+AC677DLuv/9+WrVqBUCZMmV4/PHHLzyelT+5rzl0gffzatgX8haEcfdA24fd+xIQCJf1g++vh2mvQ+P+Z9ZsyyY8TdKMMcOAq4E91toGqew3wLvAlcBxYJC1dlHWRikiIuKzYzEsH+3qh52jlMSBAweYMmVK8izM6tWrM3HiRPLly8d9991HzZo16dy5M6VLl86YmFaMhQrN3BJLuVWtrvDI+jO7NYPyQsSL8Gk7N4Gi09PexXeRvG5J+wr4APjmHPu7AzV9txbAx76vIiIiWe+vdyBfMbjiX8mbEhISCApy/07vuusuhg4dirWWggUL0rFjR66++urkY597LpWlki7FvnWwaxl0ez1jz5sdpTburOxlbuLE7I+g+Z3Zbu1PT5M0a+10Y0yVNA7pCXxjrbXAHGNMEWNMWWvtzqyJUERExCfuKKybiG1yC8vXbiEyMpKoqChmz57Ntm3bKFiwIK1bt6ZMmTJERETQvHlzgoODMzemFWMBA/V6Ze51srOOT7mJFH+9Dd2zVzJ73iTNGPMG8DJwApgANAL+ba0dnsmxAZQHtqV4HO3bpiRNREQy17h7IDAP9Pg/rLWYNRP4ffVRbn1vGLv3vgVAnTp1uPnmmzl+/DgFCxbklltuybr4Dm2DeZ+6ZakKlc2662Y3JWpC4xth/mdQubVbWzSbSE8BkS7W2iO4sWObgRrAI5kZ1IUyxgwxxiwwxizYu3ev1+GIiEg2d3T1VH4d9RX/evlj6tWuzujRo2HlT1QpV4ZOnbswbNgwtm7dyurVq3n//fczbnxZesWfgJE3QWI8XP2/rL12dtTlZVd4ePQgWDrS62jSLT3dnaeOuQoYba09bLKu3sh2oGKKxxV8285grR0KDAUICwvLHauuiohIhjty5AhXXXUVc2bPJCHREhIE7erHUSR/MCyOpF7n2/m+22veBmkt/PIv2LnMrYVZoqa38WQH+YrAzT/BiH6uXEdA4JmLvPup9LSk/WqM+RtoCkwyxpQEYjM3rGTjgVuM0xI4rPFoIiJyqay1rF27lg8//JBevXpxzz33AFCwYEGKhlgebhnMpPfu4+BPj/Fn72N0Cf0bEk+6QeheW/qDW2Gg45NQu5vX0WQfeQvATaOhYnP4/RE4cdDriM7rvC1p1trHfePSDltrE40xx3AD+i+ZMWYE0AEoYYyJBp4Dgn3X/QT4HVd+Yz2uBMfgjLiuiIjkXi+88EJydyVAlSpVqFvXFaU1xjD+piKwrzLc+V+IOwKLh7laW4UrulIXXoo7ClHPQfkwVxNMLkxwPrjyLVeWY9qb0O1VryNKU3pnd9YBqhhjUh5/rrIZ6Wat7X+e/Ra491KvIyIiuc+JEyeYMWMGkZGRzJ07l8mTJxMYGEhCQgJhYWE88cQThIeHU716dZKH8Wz6Czb/5arU58nvbpffDPM/h3o9vV9e6K+3IWY39Ps+269L6ZmyjeDyW9yki7DBft1dnJ7Znd8C1YElQKJvsyUDkjQREZGMNm3aNF5++WX++usv4uLiCA4OpnXr1uzdu5cyZcrw0ksvnfvJM/8PQktC04Gnt7V5CHavgqaDMjv0tB3YBLM/hEb9oEKYt7Fkd52ecSs1/PIgVG4FO5ZAzQhocafXkZ0hPS1pYUA9X6uWiIiI39iy5XS9sgceeIDWrVuTkJDArl27uOeee4iIiKBdu3aEhoae/2S7VsD6KFeZPjjf6e2Fy8Otf2Tei0ivyGfcclThGVwQNzcqUBLaPwoTn4ats914teh5cPlACA7xOrpk6UnSVgBlUG0yERHxA4cOHeLJJ58kKiqKdevWAVC2bFn69nWz9Tp37szy5csv/MSz3ofgUAi7LSPDzRibpsPqX1wCeY7lqOQCtboPqneColVdoja8D6z703Vr+4n0JGklgFXGmHlA3KmN1trsUw1ORESypZMnTzJnzhyioqIoVaoU9913H6Ghofz88880adKEe++9l/DwcOrVq8cllYc6HA0rfoRmd0D+Yhn3AjJCUiJMeAIKV3KJhWQMY6B0fXe/WgcoUMbNnM1mSdrzmR2EiIhISsOGDWPs2LFMnTqVY8eOERAQwI033sh9991HcHAw27ZtIyAjB87P+djVH2t1T8adM6Ms+hp2r4Drvj6zG1YyTkAgNLrO/Rwc2w+hxb2OCEhfCY5pWRGIiIjkTrt27SIqKopFixbxzjvvADBx4kTWrl3LwIEDCQ8Pp2PHjhQpUiT5ORmaoFkLS753ywUVqZRx580IJw7B5Jeh8hV+1cKTIzXq57q8V4yBFkO8jgZII0kzxsyw1rYxxhzFzeZM3oWrjlEo06MTEZEcaenSpXz99ddERkayYsUKAIoXL86TTz5JiRIl+OqrrwgJyaIB3Ps3wIkDbnySv5n0Ihw/AN1e9778R05XpgGUbgjLfvCbJO2cH0WstW18XwtaawuluBVUgiYiIumVmJjIvHnzeOWVV9iwYQMAq1at4qOPPqJMmTK8/vrrLFy4kD179lCiRAmArEvQwM3qA+8L1Z5t4zRY8AW0utfV9pLMd9kNsH0h/PUOHNvndTTpLmaLMaYUkPxbY63dmikRiYhItnf06FG+++47IiMjmTx5MocOHQKgYsWKVK9end69e9OrVy/y5fODMVbR8yFvIShR2+tITouLgfH3QbHq0PEpr6PJPRrfBGsmwKQXYMqrbhmw3p96Vjg4PcVsrwHeBsoBe4DKwGqgfuaGJiIi2cX+/fuZPHkyBQoUoHv37iQlJXHfffdRrlw5+vTpQ0REBJ07d6ZkyZJAFreUnU/0fCjf1H8q+Cclufpdh7bB4D/cqgeSNfIXg8G/wZ7VsPBrtyyYhz8X6WlJewloCURZa5sYYzoCAzI3LBER8XfTp0/njz/+IDIykkWLFmGt5corr6R79+4ULlyYjRs3UrFixUsrjZHZ4mJg90r/WAczKQn+/hWmvg57VkLLe101fMl6pepC99e9juLcY9JSiLfW7gcCjDEB1topuFUIREQkl0hKSmLp0qV8++23ydteeOEF3nrrLfLly8fzzz/PrFmz+Pnnn5P3V6pUyb8TNIAdi8Em+cd4tIlPw6ibITEO+nwGXdJYvkpyhfS0pB0yxhQApgPfGWP2AMcyNywREfHazp07mTBhApGRkUyaNIk9e/YQEBBAz549KVSoEEOHDqVUqVIULFjQ61AvXvR899XrtTD3b3ALfjceAD3ehcB0DxmXHCw9LWk9gePAv4EJwAagR2YGJSIiWe/IkSOMHz+e/fv3AzBq1ChuvfVWJk+eTEREBF999RVbtmyhUCE3wb969erZO0EDiF4AxWt4v8rApBcgKMSty6kETXzSU8z2VKtZEvB15oYjIiJZJSEhgblz5yYvUD5nzhwSExMZPnw4N910E/3796djx440bNjQ/7stL4a1rvxGjXBv49g2H1b9DB2egAKlvI1F/EpaxWxvA4pZa9/0Pd4OFMQVs33EWvtJ1oQoIiIZwVrLmjVrSExMpH79+mzbto02bdpgjCEsLIzHHnuMiIgIWrVyg9VLlSpFqVI5OGk4tAWO7fV2PJq1EPkshJbSupzyD2m1pN0FdEvxeI+1trwxJgT4E1CSJiLi5/bs2cOkSZOIjIwkMjKS6Ohorr/+ekaOHEnVqlUZP348V1xxBcWK+dmi4llhyyz31cskbfMM2DoLrnwL8hbwLg7xS2klacY3q/OU0QDW2lhjjB9UHxQRkbMdP36cNWvW0KRJEwDCw8NZvnw5RYsWpXPnzoSHh9OlS5fk43v0yKVDjGP2QNTzULwmlKrnXRzzP4N8RaGJKlvJP6WVpBVJ+cBa+yqAMSYAKJGJMYmISDolJiayePFioqKiiIyMZObMmeTJk4f9+/cTHBzM//73PwoXLkyTJk0IDAz0Olz/kJQIY++A2MMwYKx3A/WP7IDVv0KreyBYbR/yT2n9ZE40xrxsrX36rO0vAhMzMSYREUnD5s2bKVu2LHnz5uXll1/m+eefB6BRo0bce++9REREJA/079y5s4eR+qm/3oGNU6HHe25Rba8s/MrVaAu71bsYxK+llaQ9AnxujFkPLPVtuwxYANye2YGJiIhz8OBBpkyZkjyubMOGDUycOJGIiAiuu+46atSoQXh4OKVLl/Y6VP83632Y8jI0vA4uv8W7OBJOuiStRjgUq+ZdHOLXzpmk+Upv9DfGVOP0Op2rrLUbsiQyEZFc6uTJk8TExFCsWDFWrlxJo0aNSEpKokCBAnTo0IEHHniAevXcOKp69eol35c0nFoPc86HUK8nXPMBeFlW5O9fIGY3NL/DuxjE76WnTtpGYGMWxCIikitZa1m5cmVyS9m0adO49dZbef/996lTpw7PP/88HTt2pEWLFgQHB3sdbvY05RWXoDW/E7q9BgEej8+b9zkUqex9jTbxayprLCLigZiYGAoUcCUXmjZtyuLFiwGoVasWgwcPpnfv3gAEBgbyzDPPeBZnjnB0N8z+EBpcC93/620LGrgF3bfOgogXvU8Wxa8pSRMRyQIxMTFMmzYteRbm0aNH2bx5M8YYbr/9dkJCQggPD6dSpUpeh5rzzHgHEk9Cx6e8T9AA5n/uloBqcrPXkYifS1eSZoxpA9S01n5pjCkJFLDWbsrc0EREsq+EhAQCAwMxxvDmm2/y1FNPER8fT0hICG3btiUiIoKEhASCg4O55557vA435zq8HRYMg8Y3QvHqXkfjyn4sHela9bxeL1T83nmTNGPMc0AYUBv4EggGhgNXZG5oIiLZh7WW9evXJ48rmzJlClOnTqVx48ZcfvnlPPTQQ0RERHDFFVcQEhLidbi5x19vuaWX2j/qdSTO0h8g/hg0U5EEOb/0tKT1BpoAiwCstTuMMQUzNSoRkWzAWosxhuXLl9OjRw+2bNkCQOXKlbnuuuuSk7HOnTurXpkXju6CRd+6UhtF/KAb2VrX1Vm+KZS/3OtoJBtIT5J20lprjTEWwBgTmskxiYj4pdjYWGbMmJHcWtanTx+efvppqlSpQtOmTXn00UeJiIigRo0aycVkxUOrxkNSPLS40+tInE3TYN9a6KWlryV90pOkjTLGfAoUMcbcAdwKfJa5YYmI+A9rLX369GHChAnExsYSHBxMq1atqFy5MgAFCxZkzJgxHkcp/7B6PJSoDSVrex2JM+8zyFcM6vf2OhLJJtJTJ+0tY0wEcAQ3Lu1Za21kpkcmIuKBrVu3Js/APHToEH/88QfGGMqWLcudd95JREQE7du3Ty6fIX7q2D7YMhPaPux1JM7haFjzO7S+H4I1JlHSJz0TBx4CRioxE5Gc7PPPP+fNN99k7dq1AJQpU4YuXbqQlJREQEAAH330kccRygX5+1e3Lma9a7yOxFnwpRuTpnU65QKkp7uzIG6x9QPASGC0tXZ35oYlIpI54uPjmTt3bvK4stGjR1O+fHkCAwOpXr06d911FxEREdSvX1/jyrKzVT9D0apQ2sMF1E9JOAmLvoZaXaFoFa+jkWwkPd2dLwAvGGMaATcA04wx0dZarWUhItnG6tWrefTRR5k6dSoxMTEEBAQQFhbGnj17KF++PIMHD2bw4MFehykZ4cRB2DQdWt3rH8VrV4+HY3uhmdbplAtzISsO7AF2AfuBUpkTjojIpdu9e3fyuLKuXbvSv39/ChQowOrVqxkwYAARERF07NiRokWLeh2qZIY1f0BSAtTt6XUkrotz7idQrBpU7+R1NJLNpGdM2j3A9UBJYDRwh7V2VWYHJiJyIay1PPbYY/z5558sW7YMgGLFitGwYUMAKlasyPr1670MUbLK6l+hUAX/qEW2LhKi58NV70BAgNfRSDaTnpa0isCD1tolmRyLiEi6JCYmsmjRouQ1MF977TWMMcyYMYMSJUrw2muvERERQePGjQkM1ALWuUpiguvqbHit912dSUkw+UU3Dk3rdMpFOGeSZowpZK09Arzpe3zGImPW2gOZHJuIyBnGjx/PN998w+TJkzl48CAArVu3Tq78P2PGDALUWpG7bV8IJ49CtQ5eRwKrfoJdy6H3UAjK43U0kg2l1ZL2PXA1sBCwQMqPJBaololxiUgud+DAASZPnkxUVBRvvfUWBQoUYNGiRcydO5devXoRERFBp06dKF26dPJzlKAJG6cCBqq29zaOxASY/AqUqgcN+3obi2RbxlrrdQwZKiwszC5YsMDrMETkImzevJmhQ4cSFRXFggULsNZSsGBBJk+eTFhYGCdPniQ4OFilMeTchnWH+ONw5zRv41j0DYy/H/p9D3Wu8jYW8WvGmIXW2rDU9p33Y6cxZlJ6tomIXAhrLcuWLeOdd95h5syZgGs9e+ONN8iTJw/PPfccM2bMYP/+/YSFub9fefLkUYIm5xYXA9HzvO/qjI+Fqf91C6nXvtLbWCRbS2tMWgiQHyhhjCnK6e7OQkD5LIhNRHKYhIQEhg8fTlRUFFFRUeze7epiP/3001xxxRU0btyYAwcOUKhQIY8jlWxpyyxXesPrJG3hl3AkGnp96P3kBcnW0hqTdifwIFAONy7t1E/aEeCDzA1LRHKCo0ePMm3aNA4fPsxNN91EYGAgTz/9NPHx8YSHhyffKlasCLgxZUrQ5KJtnAJBIVCplXcxxMXA9Legajvvk0XJ9s6ZpFlr3wXeNcbcb619PzMubozpBrwLBAKfW2tfP2v/INzs0u2+TR9Yaz/PjFhEJGMsXryYX375hcjISObMmUNCQgLVq1fnpptuwhjD3LlzKVu2rAb5S8bbOBUqtfR2AfO5H8PxfdDpWe9ikBzjvH8lrbXvG2MaGGOuN8bccup2qRc2xgQCHwLdgXpAf2NMvVQOHWmtbey7KUET8SPWWtasWcPHH39MYmIi4BYqf/7554mNjeWRRx5h0qRJrFixIvk55cuXV4ImGe/obtizytvWq7gYmPU+1OoOFZt5F4fkGOlZceA5oAMukfodl1TNAL65xGs3B9Zbazf6rvMD0BPQagYifuzgwYP8+eefyQuUb9u2DYCWLVvSpEkTnnzySV588UWKFy/ucaSSq2zyzeb0MklbPhpiD0ObB72LQXKU9Hyc7Qt0BnZZawcDlwGFM+Da5YFtKR5Hk/qEhGuNMcuMMT8aYyqmdiJjzBBjzAJjzIK9e/dmQGgicsqJEyeIjIxMXlJp3rx59O/fn7Fjx9KsWTM+/vhj1q9fT5MmTQDXUqYETbLchimQryiUucyb61sL8z+H0g2hYgtvYpAcJz3LQp2w1iYZYxKMMYVwC62nmixlgl+AEdbaOGPMncDXwD9WqLXWDgWGgquTlkWxieRISUlJLFmyJLmlbMaMGcTFxfHMM8/w4osv0q5dO+bMmUNYWJiWXBL/YK0bj1a1vXfrY26dA7tXQI93NaNTMkx6krQFxpgiwGe4WZ4xwOwMuPZ2zkz2KnB6ggAA1tr9KR5+DryRAdcVkbNs2bKF3bt307x5cxISEmjXrh3Hjh2jQYMG3HPPPURERNCuXTsA8uXLR4sWaikQP7JvHRzd4W1X5/zPIG9haHiddzFIjnPeJM1ae4/v7ifGmAlAIWvtsgy49nygpjGmKi456wfcmPIAY0xZa+1O38NrgNUZcF2RXO/QoUNMmTKFyMhIoqKiWLduHY0aNWLp0qXkyZOHn3/+mXr16lG2bFmvQxU5v41T3VevkrSju2HVeGh+B+QJ9SYGyZHSKmZ7eVr7rLWLLuXC1toEY8x9wJ+4EhzDrLUrjTEvAgusteOBB4wx1wAJwAFg0KVcUyS3OnnyJIsWLaJly5YADBkyhNGjRxMaGkqHDh2SW8tO6dy5s1ehily4jVOhaBUoVtWb6y/9HpLiIew2b64vOdY51+40xkxJ43nWWvuPsWH+QGt3irjSGKtWrSIqKorIyEimTp3KsWPH2Lx5M5UrV2b+/PmcOHGCli1bkidPHq/DFbl4iQnwRlVo0MeNB/PCV1e7WZ13/eXN9SVbS2vtzrSK2XbMvJBEJKPt3LmTkJAQihYtyqhRo+jXrx8ANWvWZODAgURERFCyZEkAmjVTDSfJIXYsgrgj3nV1njwG2+ZCi7u8ub7kaOmpk5Zq4Vpr7aXWSRORS3Ds2DGmTZuWPK5sxYoVvPfee9x///106tSJzz//nPDwcCpXrux1qCKZZ+NUwLiZnV7YOhsST2oJKMkU6ZndmfIjdwiuZtoiLr2YrYhcgMTERPbt20fp0qU5duwYJUqUIDY2lrx589K2bVsGDBhA165dAShZsiS33abxMZILrIuEspdB/mLeXH/jVAjM4+16oZJjpWd25/0pH/vKcfyQWQGJiGOtZcOGDcnjyiZPnkxYWBiRkZGEhoby3//+l7p169KmTRvy5cvndbgiWW/rHIieB11e9i6GjVNd8do8+b2LQXKs9LSkne0Y4NEUGpGc7ejRoxQsWBCA/v37M3LkSAAqVqxInz59uOqqq5KPfeCBBzyJUcRvTHsD8peAsFu9uf6xfbBrOXR62pvrS46XnjFpvwCnpoAG4NbwHJWZQYnkFrGxscyaNSu5uv/y5cvZu3cvhQoVom/fvrRt25aIiAhq1qyJURVzkdOiF8CGSRD+gne1yTZNd1+raZ6dZI70tKS9leJ+ArDFWhudSfGI5GhJSUkkJSURFBTEjz/+yC233MKJEycICgqiZcuWPPXUUyQkJADQt29fj6MV8WPT3oB8xaDZ7d7FsHGqW2WgbGPvYpAcLT1j0qYB+NbtDPLdL2atPZDJsYnkCNHR0ckzMKOionjvvfe44YYbaNiwIXfccQcRERG0b98+uZtTRM5jxxJY9yd0egbyFvAujo1ToWpbCLyYkUMi55ee7s4hwItALJAEGFz3Z7XMDU0ke7LWYozh4MGDtGrVijVr1gBQunRpunTpQoUKFQCoXbs2777rUfFNkexswRcQHArNh3gXw+pf4NAWaPOgdzFIjpee9P8RoIG1dl9mByOSHcXHxzNv3rzkcWU1atTg66+/pkiRIrRo0YIhQ4YQERFBgwYNNK5M5FKdPAYrxkL93hBSyJsYju6GX/7lSn80udmbGCRXSE+StgE4ntmBiGRHDz74IMOGDePo0aMYYwgLC6NBgwYAGGP4+uuvPY5QJIdZNR5OxkCTm7y5vrUw/n6XLPYeCoHB3sQhuUJ6krQngFnGmLlA3KmN1lrN/5dcY8+ePcn1yubPn8+SJUsICgqidOnS3HjjjURERNCxY0eKFfOooKZIbrF4OBSrlvnFY5MSYUQ/CC0FvT5Mcf1v3Xi4bv+FUnUyNwbJ9dKTpH0KTAaW48akieQav//+O08++SRLly4FoGjRonTu3JlDhw5RokQJnnjiCY8jFMlFDmyELTPchIHMHjow+0NYNxECgiDiBQgt4VrRZvwflG/q7Xg4yTXSk6QFW2sfyvRIRDyUmJjI4sWLk1vLnn32Wdq3b09oaChFixbllVdeISIigssvv5zAwECvwxXJnZZ8DyYALuufudfZtQImvwTlLncLuK/8CZrf4RZSP7ABen4EAQGZG4MI6UvS/vDN8PyFM7s7VYJDsr19+/Zxzz33MGnSJA4ccD/SjRo14tixYwC0b9+eKVOmeBmiiAAc2goLv4bqnaBw+cy7TkIcjB0CIUXgptHwTU9Y+oNL0hZ/62aV1uuZedcXSSE9Sdqpjywp+3VUgkOynYMHDzJlyhQiIyOpUqUKjz32GEWKFGHlypX06NGDiIgIwsPDKV26tNehikhKBzbC19dAYhx0fjZzrzXjf7BnJdw4ynVxNroBIp+BnUth5Tho0Nvb2mySq6SnmK3W6ZRs7e2332bUqFEsWLCApKQkChQowK23urX+goKCWLlypccRisg57d8AX10NCSfglvGu7EVmObAR/noHGlwLtbq6bQ2vg8hnYcztblZp4wGZd32Rs6SnmO0tqW231n6T8eGIXDxrLStXriQyMpJly5bx5ZdfArB06VKCgoJ45plnCA8Pp0WLFgQHa9q8iN+zFsY/APHHYdBvUKZB5l7r90cgMA90eeX09kJloVp7t7pAsepQqWXmxSBylvR0dzZLcT8E6AwsApSkiV+YNWsWn3zyCVFRUezcuRNw1fwPHjxI0aJF+frrr1VEViQ7Wv2Lm8151duZm6Cdutb6KOj6mkvMUmrUzyVpTW7K/FmlIimkp7vz/pSPjTFFgB8yKyCRtMTExDBt2jQiIyO5++67qV27Nlu3bmXChAmEh4cn3ypVqpT8HCVoItlQQpwbC1ayLlw+KPOvF/U8lKqfemmNBn0gZheE3Zr5cYikcDGrwh4DNE5NssyBAwf48MMPiYyMZPbs2SQkJBASEkKbNm2oXbs2ffv25frrrydAU+JFsqekJNi+EFaNgxOHoHpH2LcWDm6Gm3/K/AXMD211pTW6/Tf1awXlhTb/ztwYRFKRnjFpv+BmcwIEAPWAUZkZlORe1lrWr19PZGQkpUuX5tprryUwMJCXXnqJhg0b8p///IeIiAiuuOIKQkJCADf4X0SyqSM74asr3aD9gGDIEwpLhrt9tbq5khuZbcts97Vy68y/lsgFSM9/t7dS3E8AtlhrozMpHsmlfvrpJ3777TeioqLYsmULANdffz3XXnsthQsXZt++fRQq5NFiyiKSeeZ+4lrMen4Eda+GPAVg+yLYMhMaXZ81MWyZCXkLQ+n6WXM9kXQ6Z5JmjKkBlLbWTjtr+xXGmLzW2g2ZHp3kSLGxscyYMYPVq1dz//1uyONHH33E/Pnz6dSpE4899hjh4eHUqFEj+TlK0ERyoJPHYOFXULfHmQumV2zmblllyyw3azNAq4mIf0mrJe3/OLOA7SlHfPt6ZEI8kkOtW7eOn376iaioKP766y9iY2MJCQlh8ODBFChQgOHDh1O8eHF1XYrkJstGQuwhaHmPdzHE7IH96+Dym72LQeQc0hppXdpau/zsjb5tVTItIskRtm3bxrBhw9i7dy8AEyZM4LHHHmPHjh3cdddd/Pbbb+zdu5cCBVzl7tKlSytBE8lNkpJgzsdQtjFUbOFdHFtmua+Vr/AuBpFzSOu/YpE09uXL4Dgkmzt+/DiRkZHJt7Vr1wIwYsQI+vXrx4ABA7j22mspV66cx5GKiF/YONnN4Ow91NvaY1tmQXD+zF3JQOQipZWkLTDG3GGt/SzlRmPM7cDCzA1L/F18fDxz5swhX758hIWFsWfPHnr16kVoaCjt27fn7rvvJiIignr16gFQtGhRihYt6nHUIuI3Zn8EBUpD/d7exrFlFlRsDoFahUT8T1pJ2oPAT8aYmzidlIUBeQCPf6vEC6tXr05uKZs6dSoxMTFcd911jBo1iipVqjBr1iyaNm1Knjx5vA5VRPzZjsWwYZJbLD3Iw78XJw7C7hXQ8UnvYhBJwzmTNGvtbqC1MaYjcGo9jt+stZOzJDLx3K5du1i1ahWdOrk6Rf369WPZsmXUqFGDAQMGEBERQceOHZOPb9WqlVehikh2Mv0tCCkMze7wNo51kYBVfTTxW+lZFmoKMCULYhGPHT9+nOnTpye3li1fvpzQ0FAOHjxIcHAwn376KWXKlKFKlSpehyoi2dWe1fD3r9DuUQjxqLTOoa0w+WU3u7RwJSjf1Js4RM5D0+lyscTERBYtWkSDBg3Ily8fr732Gi+//DJ58uShTZs2vPbaa0RERBAY6GoHtWzZ0uOIRSTb++ttCA6Flndn7XV3r4Q5H8HWua7kRlAItHkI2jwIwZoLJ/5JSVous3HjxuSWssmTJ3Pw4EEmTJhA165dGThwIG3atKFt27bkz5/f61BFJKfZvwFWjIFW90L+Yll33SM74dveEH/CldpofCM0vA6KVMy6GEQugpK0HO7AgQPExsZSrlw5Fi9ezOWXXw5AhQoV6NWrFxERETRr5ip716hR44wq/yIiGWryy64Fq9X9WXfNhDgYdTPExcDtUVC6XtZdW+QSKUnLYeLi4pg1a1Zya9nChQu55557+OCDD2jUqBEffvghnTp1onbt2hgvaxOJSO6yfSGsHAvtHoGCpbPuun88CtHz4bqvlaBJtqMkLZuz1rJz587kIrENGjRg/fr1BAYG0rJlS5577jl69HAreAUGBnLPPR4uvyIiuZO1MPFZyF8CWj+QddfdOtetDXrFg1C/V9ZdVySDKEnLhrZv357cUjZp0iTy5MnDli1bMMbw3HPPUbhwYdq3b69FyUXEP6ybCFtmQPc3s3ZG5/zPIG8haP9o1l1TJAMpScsGjh49SmhoKAEBATz77LO89NJLAJQqVYrw8HDCw8NJSkoiMDCQAQMGeBytiEgKx/bBhMehWDVoOijrrhuzF1aOg2a3QZ7QrLuuSAZSkuaHEhISmDdvHpGRkURFRTFnzhzmzZtHkyZN6NKlC4ULFyY8PJyGDRsSEBDgdbgiIqk7fgC+6eVmV948NmtXF1j8DSTFQ9htWXdNkQymJM0PWGuJj48nT548LFy4kE6dOnHkyBGMMTRt2pSHH344ed3LNm3a0KZNG48jFhE5j9jDruzFvrVw4w9ZW9U/KREWfAlV20PJWll3XZEMpiTNI3v37mXSpEnJrWW33norzz33HHXq1KFfv36Eh4fTqVMnihcv7nWoIiIXJu4oDL/WFZDt9x1U75S111/7JxzeBl1fzdrrimQwJWlZxFqLMQZrLW3btmXmzJkAFClShE6dOtGkSRMAQkND+fTTT70MVUTk4p08Bt9dD9sXwfVfQ62uWR/Doq+hYFmofWXWX1skAylJyyRJSUksWbIkeRZmbGwsM2bMwBhDmzZt6N69OxERETRt2jR52SURkWwtZi/8OBi2zYFrP4e6PbI+htgjsGEyNB8CgfoXJ9mbpz/BxphuwLtAIPC5tfb1s/bnBb4BmgL7gRustZuzOs4L9c477/Dqq6+yf/9+ABo2bEiXLl1ISkoiICCA119//TxnEBHJZtb+CT/f68ai9foEGlzrTRzrJkLiSahztTfXF8lAniVpxphA4EMgAogG5htjxltrV6U47DbgoLW2hjGmH/Bf4IasjzZ1hw4dYsqUKcnjyiZPnkyFChUoU6YMV111FREREYSHh1OmTBmvQxURyTzzPoPfH4bSDeCW8d5W9v/7VwgtBRWbexeDSAbxsiWtObDeWrsRwBjzA9ATSJmk9QSe993/EfjAGGOstTYrAz3b4sWLufvuu5k/fz5JSUmEhobSoUMHjh49CsCNN97IjTfe6GWIIiJZIy4GprzqZlLeNBqC8noXS3wsrIuEhn0hQMNIJPvzMkkrD2xL8TgaaHGuY6y1CcaYw0BxYF/Kg4wxQ4AhAJUqVcqseJOVKlUKYwxPPfUUERERtGjRgjx5srD+j4iIv1gwDE4cgM7PepugAWycCidjoI4HY+FEMkGOGFVprR0KDAUICwvL9Fa28uXLM3v27My+jIiIf4s/AbPeh2odoUKY19HA37+4ZaCqtvM6EpEM4WW5+u1AxRSPK/i2pXqMMSYIKIybQCAiIl5b9A0c2wPtHvE6EkhMgL9/dyU/snJlA5FM5GWSNh+oaYypaozJA/QDxp91zHhgoO9+X2Cy1+PRREQEN4tz5rtQqTVUucLraFzZjxMHNKtTchTPkjRrbQJwH/AnsBoYZa1daYx50Rhzje+wL4Dixpj1wEPA495Em4K1MOJGmP0RJMZ7HY2ISNY7tg++7gExu6HzM15H46z5AwLzQI1wryMRyTCejkmz1v4O/H7WtmdT3I8FrsvquNIUdxQSYuHPJ1xV626vQ/WOXkclIpK5ju2H4/vgxEEYfz8c2gr9RmTtmpxpWTsBqrSFvAW8jkQkw+SIiQNZKqQQDBjjPrX9+QR828t9cgt/Hso09Do6EZGMt2EKfHcdJPl6D/IUhAFj/aObE2Dfeti/Hprf6XUkIhlKSdrFiF7gPj3eMxfmfQp/vQOftIXGN0LEixBawusIRUQyRlIi/PkUFCrnymzkCXUfSAtX8Dqy09ZOcF+9WCdUJBMpSbtQJw7B8GuhZG24eSxc8S+4/BaY8T83Tu3v36DDE1CkopueXqqet9W3RUQuxbKRsGcl9B3m3VJP57N2gvtbW7Sy15GIZCgvZ3dmT/mKQM8PYPtC+L4fnDwO+Yq6FrS7ZkDp+jDhMfjhRhhzGwxt77pGRUSym/gTMPllKNcE6vX2OprUnTgEW2dDrW5eRyKS4ZSkXYx610DvT2HLTJeMnTzmtpeqA4N+gyHT3O2umW4tu5EDYMUYb2MWEblQcz+BI9vdh9AAP/13sT4KkhKUpEmOpO7Oi9XoOkiMc7OcvrwSbhwJBcuAMVCu8enjbvkZvr8BxtzuxnY0ut6zkEVEznBkJ0TPh+h5sHMpVG4Dbf7tisEu+hYmvQS1uvt3Bf+1EyB/cf9Y8UAkgylJuxRNBrg/Dj/eBp91dolamQZnHnNqNuj318NPd0FwPqirdeVExCNxMTDhcTdj80i02xaYB4pVh6mvwqqfoVp7mPMRVO8M137mbbxp2bcOVo5zf4u1oLrkQCanFfAPCwuzCxYsyNqL7lzqWsviYuC6r6BmKsUU42Lg296wYzF0fcV1kR7cDA2vg6ptszZeEcmdTh53Hxi3zIJ6PaFic6jQzM3WDMoLaybArw/C0Z1wWX+45n0IDPY66tRZC99cAzuWwv0LoEApryMSuSjGmIXW2lSbgpWkZZTD22HEDbB7JXT7L4QN/ucftxOH4OurYddy9zg4v1u1oM9QaNAny0MWkVwkPhZG9IONU6HPZ27IRmpOHIRt86FmhBu+4a+WjYaxt8OVb0HzO7yORuSiKUnLKnEx8OOtsO5PCAqBspdBoxug2W2nj4k/AXv/hmLVfEtM9YOtc1zZjuLVIfGke17p+t68BhHJmcbf7xZE7/kRNLnJ62guzYlD8EEzKFwebp+krk7J1tJK0jQmLSPlLQD9R8Dq8e6T6Oa/4LeHXGtZy7vcMcH53HT2UwaMhR8Hu7EgKVW+Alrc5cav+fOnWRHxf7tXuYkALe/N/gkauFmnx/bATaOUoEmOpiQtowUEQv3e7paYAKMHurpp+Yun3r2QJ79b/27/OjABgIE1v8P8z2DUza5Q7lXv+O+4EBHxf5NehLwFod3DXkdy6RLiYP7nULPLmR94RXIgPy18k0MEBsG1X7hFf8fd5WZTpSYgwK1gUKImlKgBVzwADyyBdo+47onvr4fYI1kauojkEFtmw9o/3Ooo+Yt5Hc2lWzEGju2Flnd7HYlIplOSltmCQ6Df91CiluvWPLg5fc8LCIROT8M1H8Cm6fB559MTDkRE0sNaiHoeCpTOGUmNta40SMk6UK2j19GIZDolaVkhpBDcMBxsklt94OTx9D/38pvh5p9cS9pnnWDOJ+4PlYjI+awYA9vmQPvH3MLo2d2Wme7Dasu7NVZXcgUlaVmleHXo8znsWgHf9oJx98DP97m6aedTtR3cPdMVlpzwGIy/z413ExE5l2P74I9HoXxTaDrI62guXVISzHwP8hVzs+ZFcgElaVmpVhfo9joc2QEbp7lK2d/0gj2rz//c0BJu5mj7x2DxcDchIT42syMWkezqj0ddC3zPD7P/DMgjO2B4b1feqNU9bpa8SC6gJC2rtbwL/r0CHloJd89wVb6HXwuHo8//XGOg45OuWO7fv7rCuOlpiROR3OXv311XZ7tHoFRdr6O5NJtnwsetYds86PEutM0BM1RF0knFbL22a7lboD2kiKvwXboe1OoGhSuk/bwVY+D3R+D4fmjUD9o/6rpURSR3O3EIPmzhyv4MmeoWS8+ujh9wCVqeUOg/0s1+F8lh0ipmq5Y0r5VpCDeOcknZ8tHw23/g/aYw5VW3vue5NLgWHlgMbf4NK39yz/m+n+tGzWGJt4hcgIlPu0KvPT/IPglawknXm7B90Zkz4H9/xJXbuPYLJWiSK6klzZ9YC/vXw9TXXEtZoQquovb5log6uhsWfAHzv4Dj+6BUfTf7qcG1rliuiORM1rrkJm9ByFfE1WL8tperiRbxotfRpc/eNfBFF4g95NtgoGFftzzexKeh49PQ/hEvIxTJVFq7MzvaMtvVVUuMh0G/Qak6539OfCys+BHmfAy7V0BwKNTu5pK1ml1dcV0Ryf6iF7gPZZv/gsPbICAIKreG/RvdONe7Z2afwfVjbndj6Lq+4uq5bZsL84ZC/HE3M/XWifrbJTmakrTsat96+OpK92l50G9Qslb6nmetqye0fDSsGg8nDrhWuWa3Qa2uEFrKVR7P7jO+RLKrhDj44SYIKQy9P0n/sm/7N8CkF2DVz+65Vdu7dX5jdsGaP9z+geNdwpYd7N8AH4RBq3uhy8unt8fsgSXfuw+YRSp6F59IFlCSlp3tXQtfXQVYGDDGdQFciMR4WPsnzPvUrVxwSlAINL/DzZTKVyQjIxaRtFgLvzzglnwDaNAX+gw9/4emNX/A6MFulnfrB6D1fa6bM6X4WLfKSXYx/n5YOhIeXAYFy3gdjYgn0krS1Ibs70rWgsG/u3pqX13taqVVaZP+5wcGQ92r3W3vGti90hW53L4AZn0Ai79zy081HezWEBWRzDX/c5egtX3YzVqc9ALkLQBX/e/cv4MLhrlJRWUbu2XmCpVN/bjslKAd3g5LRkDTgUrQRM5BSVp2UKIm3DYRvu0N3/aBG3+A6p0u/Dwla7sbAEOg1X3w55Pw20Owerwrenm+0h8i4qyd6NaRrNjcVfQvVO7M/XFHYfcqKFbNFaPet9Z14c3+AGp1h45PuaQs7ijMeAeiF0KXF6FCM1gf5W6Ho13X355VULMLXPdVzljeCWDW+26pvNYPeB2JiN9Sd2d2cvwAfN0DDmyCQb+4QbWXylpY+CX8+bQbfBzxAlw+UK1qIudydLer5r9qnBvoHrMHTADU7eGGEFS+whWb/v1ROLrDPSdvYYg7DCYQaneHXh+7NX3B/Q6uGONa1A5tdb+HSQlu+aPi1d0Y0nKNoc1DOWcA/cEtbixaw+uh14deRyPiKY1Jy0mO7nLT1U/GwK1/ula2jHBgI4x/wM0WKx8GV79z4ePfRHK6hDj4tJ37oNT+EWj9LzgS7WZaLh7uykgULAtHd0LpBtD2PxCz27WiFa8BDa+DAqXOfe6FX8GR7a6gdcUWOXdyz5jbYfWvcP9CKFze62hEPKUkLafZv8ElasbANe+7T+YZwVpYNgomPuWqlnd7DZrd7q4jIjD1dVfH8MbRbi3elE4edy1iK3+Cau2h5T3pn7WZm2xfBJ91dAls52e9jkbEc0rScqI9f8OY21w9tMY3QddXM26W5vED8NNdbjHjRjfA1f+norgie9fAJ22g7jXQ9wuvo8merHVDNvasciumhBT2OiIRz2lZqJyoVB24Y7L7NLp0BHzQDJaNzpglofIXg/4/uErfy0a58TciuVlSkhsOkCcUur3udTTZ15o/3JCK9o8rQRNJByVp2VlQXtddcMdkN65j7O3uU+rWuZd+7oAA35ib+2Hxt27mmUhuNfkl2DYHurwCBUp6HU32dPKY+8BXsi6EDfY6GpFsQUlaTlCuCdw+Ca56G/ashmFdXKmOHYsv/dztH4UCZeD3/0BS4qWfT8SfbV/oahK+XglmvguJCe7rjHdcLcHGN3odYfY17Q23hNXV72isnkg6aUxaTnPymCuWOfNdN7as8U3Q+ZlLKxa5bLRrpevxrqsHJZJdWetW4JjzkatPFhDkuwVCQixEz4f8xaF0fbdCR9GqcHAT1O8D136ec2dbZrbdq+DTttCon0puiJxFEwdyo9jDMP0tt9h6UF5o+xC0vPfiKpJbC19e6VoZStaGwhWhTEO38kGFZhCYxxWlDAjUTFDxX5tnwKQX3QLeRatA8ZquHllSgmsltklQIxxa3gV5CsDqX1z3XNnL4PpvISiP168gezp+AIb3gYOb4b6FEFrc64hE/IqStNxs/waY+Ays+Q2KVHKrDFRtByXrXFhCdWiba507tMUV3Ny31v1TS6l8mGttKFY1Y1+DyKXYscQlZxsmuRpm7R+DJgPS1+WWlOR+T/Th4+Ls+RtG3ABHdkDfL93ydCJyBiVpAhunulUFdi93jwuUgfDn4bJ+F/cPKPYwbJ0DO5e6lrakeJg3FCzQ8wOoc7VWLRBv7VsPU152dcvyFYU2/4bmQyA4n9eR5Q5rJ8KPt7rvd7/voWIzryMS8UtK0sSx1nU5bP4LFn0L0fOg9lVw9f+gYOlLP//BzTB6kJuwEJgHCpV3rXbdXledNck6sYch6gVXvT8oBFrd42Ypq+RD1rDWrU868Rk3LKL/CK0JLJIGJWnyT0mJbrzapBfdP68bhkOlFpd+3oQ4WPoDHNjgls5Z7VtjtP8PKl0gmePgFji2zxVz3rsGfvsPxOyCsNt8s5PPsQyTZLzD0S5BXj7KFf3t/UnOWRBeJJMoSZNz27MafrjRjTm7+h24/JaMPf/qX2DMHe4f5YCxUKJGxp5fci9rYcEX8Mfjrrv9lFL14JoPoEJT72LLbY7ucrXklo50Y1XbPeLG/mnIg8h5KUmTtJ046MaObJgMTW6G7v/N2E+/0Qvh++vd7M9bxrvVElKyFhJPulmoKSXEwfpJsHKsa5ULzucGe8cehmN73TGl6kOZBtCg7z/PKzlX/An49SFY+j3U7OJazWIPAQbq99ZMzKxkLXxzjSui3XSg61ouUsnrqESyDSVpcn6JCTD1VfjrHSheA/oOg7KNMu78e/52f8iTEqHXR65bauMUN/v06C5IjIP8JaBIRVe36sQhOLoTTsZAvmIulsR4l7iFFILQku7x7pWwf537R9GwLzS4FjZOg3UToVg16PgklL88416HZJxDWyFmjxu7WKC0a3Wx1iVgsYfcz0DsIfchIm9BqNTKJenRC2Dc3W6GcfvH1WLjtfWTXImNbv915UtE5IL4XZJmjCkGjASqAJuB6621B1M5LhHwTUdkq7X2mvOdW0naJdo4DX660/2DvGE41AzPuHPvW++WrTq6wz0uWde1ghUs6/4JH9nh/nHbRAgp4hKxWl2hWoe0yyUc2w+z3oW5QyHhBATmhaptXV23EwehRgQUrw55C7nls8o0dF1imuXnDWth0dfw+yOuBRWAUzOM0/h7lK8oVGwJ6/50PzM9P4DqnTI7WklLUhJ81sH9nt234J+t4SJyXv6YpL0BHLDWvm6MeRwoaq19LJXjYqy1BS7k3ErSMkDMXhje27V+9RkKDfpk3LmP7IAts6BiC9dqlpFi9sCuZe4fed4CEHvETY5Y8p37JxJ35PSxAUFuNYZ2j7g4Ek7CgY1uFlreC/qRy1kS4uDvX90A8Fb3ZXyF/SM7YPIrsGS4S7Ca3e62xexx+41xMzLzFXFJWYjv6+FoWPWzKyVTqwt0fVWzNf3BijFuqETvT105HxG5YP6YpK0BOlhrdxpjygJTrbW1UzlOSZpXThyCEf1cLbQbvoW6PbyO6NIlJbpivLtWuK7WxcPd9lJ1XUKaGOeSt/JNXRJZrCoUqezKiSTEuta+ii1yVmHT4wdgXSQc2e6WP1r9i0towa1Q0e3VSzu/tW6ppSXfuwTr4Ca3vf1jvm5KLbOUbZ04BJ91hKB8cNdfei9FLpI/JmmHrLVFfPcNcPDU47OOSwCWAAnA69bacec7t5K0DHTyOAzrCsf3u66MnFbr7NA2t3D2/vVu6Z9S9WHfGtj0F+xc4pYLOluDvq6uXEihM7cf3OxmtRWrlhWRZ4yYve79PbDBPQ4p7Fq3mtzs1rec9yl0fxNaDEn9+fGxbuxf4fJQusGZXV2J8S4JnvOx+54G54dqHaHKFe4apepm/uuTjJWUdHrs3/ZFribike1w02h1O4tcgrSStKBMvGgUkNqq3k+lfGCttcaYc2WKla21240x1YDJxpjl1toNqVxrCDAEoFIlzSrKMHnyu5meX3Z3xSnbP+p1RBmrSEWXcKUmKTHFGLkk1wW3cQpMfR22L4AWd7mkJPYIrB7vxr+BK97b7Hao08O/B7PHHYXv+vr+yY6Byq3OnNFbrQMc3gYTHnNfKzRzkzeKVHYtiTuXwtg7Ye9qd3xgHpeolb/cHbPwS9d9XK4JXPO+m3GZt6AnL1UywOaZ8G0vt/h8sWqudTS0FAyeoJUERDKRX3d3nvWcr4BfrbU/pnWcWtIywcibYX0U3L8ICpX1OhpvbZ3j6r4d3np6W5lG0PA6Nwh+4VcuqanbA3oP9c/Wx9gjMPIm94+3/wg3OSM1J4/BqIGuNItNdNtCCrtJF9Hz3Wzc7v91Sdv2ha51ZccSOHnUJWydnnHnzkndw7lRYjx80gbiYtyHkP3r3AL13d+A/MW8jk4k2/PH7s43gf0pJg4Us9Y+etYxRYHj1to4Y0wJYDbQ01q7Kq1zK0nLBAc2wYfNoV4v6PmhalAlJrhabYkn3TiclBXtkxJhzkduSZzyTV0SlBkV7/eudYO29/7tWqxqdHblKIJD0n7etnkw5naXSPb6OH2DveNPwO5VsGsp7FwGu5ZDyTrQ5aV//pNOSnKtc4XK+3dLoqTfrPdh4tPQbwTUudLraERyHH9M0ooDo4BKwBZcCY4Dxpgw4C5r7e3GmNbAp0ASEAD8n7X2i/OdW0laJol6Hmb8D/IUdAlB/d5Q+0olbOey+leXDIWWdBMvyjXOmPPGxcD0N2D2hy4hLFrFlaPYOgtK1IKr3oZyl/9zhurBzTD3U3crXB76fAaVWmZMTJL9WetazM7+fT6yEz4Ig8qt4cZRahUVyQR+l6RlJiVpmSQpyQ0SX/M7rJ0AMbvd+JT6fdwg8CKV3TI8+Yp6Han/2L7IdRUf2+uW3Goy4OLPZa2beTnhCTgS7cqHhL9wej3U9ZPgl3+5FjKAvIVd13TBsoB19e9MAFzW383YVPmK3OtwtFut4eBmVyz6ZIxL/m0i1LnatbCGFHLjFkcPhk3T4J45rtagiGQ4JWmSsZISYcMUWPwtrPnDla4AN5C43/caSJzSsX2ujtSmaa6lq/IVUDPCtUKmt1Vi/wb441E3LrB0A7jyLTfQ/2xxR937cTjardZwZIe7nYyBej2h6WDXiia5147F8H0/iD8O1TtCngLulreAm6079xMoURM6PO5azw9tdSsJnGuGr4hcMiVpknmSklxCsPdv+O0hlxT0eNe12KhrxElKhPlfuJbIbXNdUd1a3eGa906PVzuwCRZ94+qJBeWBZndAnatc9+SCL1wtqo5PQvMhEJhpk7IlJ9swGX64ybWA3zgKStf75zEbp8Hoga5WXuFK0OdT19UpIplGSZpkjeMHYNQtsPkvKFzRzQRr2Fc1lFJKSoR5QyHyOVeSouxlLsE9st11R9bs4rqetsxwx5tAuPwW6PAEFCztbeySfcUdhffD3HCEW35O+2fp4GZYOQ7CBqtbXCQLKEmTrJMY75ZhWj/JJWsnDrk6WZff7HVk/mXPavjtP65VrVQ9t55o/T6nuyN3LnUtb3WvgZJpVqcROb/IZ2Hmu3D7JKiQ6v8CEfGIkjTxRvwJGDnAjaW66m1X5FVEsta+9fBRS2h0PfT6yOtoROQsaSVpKmQkmSc4n5tIUKu7azVaMMzriERyF2thwuPudzH8ea+jEZELpCRNMldQXrj+G6jZ1SVqf//udUQiuceKMbA+0i1mnxlFlUUkU2mamGS+oDxw3Zfw1dWuHMX137gZivvWAxbyFnJFX1VnTSTjHNrm6qFVaO7WmhWRbEdJmmSNPKFu2v8XEfD9dec4yLg6YKXrQYHSLnk7sNEt4p0QB4XKQcEyEJwfAoJcYle7u1uiSOU+RE5LSoJxd7sCtX0+VdkWkWxKv7mSdQqUhFsnuIkERSq74q4BQRB32BVg3ToHtsyErbPh6G5XJLdAaZeEFQx1Ndh2rXDbkxLdzMhJL0Cx6nDFA9DkFq0XKQIw+wM3u7rnh1CsmtfRiMhF0uxO8U/WQkKsG/B8Lkd3wd+/wdIfIHqeq+bf4Qm3ZNXeNVChGdTqknUxi1yIRd+67v26V2fseXcth6EdoXY3uP5btTKL+DmV4JCczVpYPBwmPg2xh87cV/sq6P5fKFLRk9AkBzt5HDZNh0otLnws5bzP4PeHXUvyzeOgatsLv761bnmn2CMQWgICg13Zm6Ed3YoBd8+C0OIXfl4RyVJpJWnq7pTszxhXLLdWV9dVWqw6FKsK8z+Hqa/De42heE1XFLZCM6hzpbqA5OIlxMHCr+Cvt12rbUhhaPsfaH4nBIec//krx8Hvj7gZzwc3u1U6hkyBolXO/9ykRFcoesEw2DAJEk+67QXLuskBBze7MZwDxihBE8kB1JImOduhrbDgS1fhf+9q908MoFR9txZmRnc1Sc6WmABf94Cts6ByG2h2m1tvdX0kFKoAnZ6CRje4tVinvua64au0g5oRcPKY+xCxbCSUuxxu/smte/tZJzcp5pbxbtzmuUQvgJ/uhP3rIbQUNOjjkrM8obD6F9g0zR3X4i7Xeiwi2YK6O0VOObjZ1Wpb9I1L2mpfCa3udetlxh2Bii2gaGWvoxR/Ne0NmPKKW+qsyc2nx3ttmu6WXtqx2E2KORwNQSGuG3PrbIg97I4LKQLVO8JV70D+Ym7bhinw/fVuLder3ob6vd3yakd3uv0BwW6ptSmvQqHyEPEC1LnalbZJaccSl6g1H5L2WE4R8StK0kTOlhgPcz5y3aHxx8/cV7Wdaw2p2g6KVPImPvE/0QtdCZkGfeDaz/+5PykJVo2DuZ9CuSbQ9iFXQDYxHrYvgpBCUKJ26jOQ96yGcffAjkWudSxmjyufkVKDa11yl69IZrw6EfGIkjSRczm8HXavhPzF3eoIa353rRanukULV4KStVwpkOI1oOXdaqXIjeJi4NN2bjza3TMzJ1FKTIB5n7rWuKJVoHBFN7Eg8SQUrgA1wjVTUyQHUpImciGSkmDPStgyy90ObXEtG0e2uxaSG76DwuW9jlIuxpGdbvzWhkkuMS9d39XrK1DKdUVumOTGmB3cDFe+BfV7wfEDrjty+0I3buxiZmKKiJyDkjSRjPD3bzB2iFvxoOOT7h98gVKwbT5snu5m+V3xoCuHIP5l61yY/qYrpIx1s3vjYuDYnn8eW7KuG++1c6kb37XpLziwAfoOg7o9sjx0EcnZlKSJZJQ9f8PIm9wMu5RCCrt/+nlCoc2D0Or+fw7slqyVlAjrIk9X389fHJrd4caUlaztjonZ6xKwmD1wfB+UvczNvEyMd3X35n0KeQpAv++gWgdPX46I5ExK0kQyUlKi6w7bt9YtVVWuifvnvn8DRD3nxrXVvhKu+1qJmhdO1TGb85F7nwqWhVb3Qdhgl0RfiPWTXHmMUnUzI1IRESVpIllq/ufw23+yd6JmbfYYpH7yGMz4n/taqp6bETn9bTi8FSq2hBZ3ui7KwGCvIxURSZVWHBDJSs1ud0nO7w+7avLXfem/M0KP7ICN0yAg0MW8fQFsmOyWGrptolu5IbMc3eXKnxStenEJ4fZFMPYO14IZFAIJJ9z2so3hmvdcPTIRkWxMSZpIZmh+h/v6+yPwbR/oP8L/6lvtWwdfXQ0xu05vC87vFqqPng8/3AS3R154F+H5JCXB3E8g6nlIjHPrXpZt7NZXLVTeje8zAYBxyZsxbnuNcNciFnsEZrwDs953pVEG/gKVW7sq/ycOQPmw1GuRiYhkM0rSRDJL8ztcVfmxd8KX3aHnh278WkZ0I+5Y7MZLnTwGCbGuSn3F5ul//t61bnkjmwi3/gmhJSEpwdXnCsrrzv1dX/j5Xuj75aXFbC3sWeWW6IrZAyvGuMr4ta+Eml1cAdedy1y9utRmW54SWgrqXAV//wrH9sJlN0K3V08vbl6ixsXHKCLihzQmTSSzbZwKI292y04VrwlNBriB7IEX8Rlp6xy3NNGGSe5xQBCYQJdsdXnZrdt4voTq79/hl38BFgb+CqXqpH7cjP+51q5a3V29sBoRpxftTkqChV/CvKFQso5b3L5GuCtJcmr/xsmwfIyLNWb36fPmKQBdX4XLb/lnrAkn4WSMu2+TXIJnk2DnEreU15o/oEIzl5yVb5r+75uIiJ/SxAERr504CKt+hmWjYMtMl9D0Hea69lJKiHOJV0Dgmdv3/O1mjq6dAPlLuPVGwwa7AqxxR+Cnu2HNb1D7Kqh3DZRp5Iq0nkoErYXdK2Dyy+4cJeu4SQ3nStBOPWfyy7D4W5dkmQCo0NwtFr5uImyb62a1Ht19usu03OVQqaXbv3+9i69GZ6je2c2QDC3pErmgvBf3fUw46bo8s8OkBhGRdFCSJuJPFn7lZn8Wr+GKpcYfd0nQtnluMHyBUtDuYWg8wHUFzv8CVvzoWqDa/NvNWDx7nFhSEsz8H0x/6/RapEEhruBu4Qru3Ed3QnAodHjcLW+V3hmPSb6WrLV/wto/XJHXfMWg6ytwWX93zK5lsHaiS86i50OFMGh+J9TrmT1nt4qIZBElaSL+ZtN0N/PzxEH3OCDYjVer2NwlVNHzXEIVfwzyFoImN0Pb/5zubjyXxATYv86N8dq1zCVUh7a4rsHqnaBWt9NdkhcrZo+brZq3YOr7E04qMRMRSSclaSL+6OQxN1MxT6i7neritNYtX7RslFsnssG1GT/DUkRE/ILqpIn4o1PJ2dmMceO+akZkfUwiIuI3VExIRERExA8pSRMRERHxQ0rSRERERPyQkjQRERERP6QkTURERMQPKUkTERER8UNK0kRERET8kJI0ERERET+kJE1ERETEDylJExEREfFDStJERERE/JCSNBERERE/pCRNRERExA8Za63XMWQoY8xeYEsWXKoEsC8LriPpp/fEP+l98T96T/yT3hf/kxXvSWVrbcnUduS4JC2rGGMWWGvDvI5DTtN74p/0vvgfvSf+Se+L//H6PVF3p4iIiIgfUpImIiIi4oeUpF28oV4HIP+g98Q/6X3xP3pP/JPeF//j6XuiMWkiIiIifkgtaSIiIiJ+SEnaeRhjuhlj1hhj1htjHk9lf15jzEjf/rnGmCoehJmrpOM9ecgYs8oYs8wYM8kYU9mLOHOT870nKY671hhjjTGawZYF0vO+GGOu9/2+rDTGfJ/VMeY26fj7VckYM8UYs9j3N+xKL+LMTYwxw4wxe4wxK86x3xhj3vO9Z8uMMZdnVWxK0tJgjAkEPgS6A/WA/saYemcddhtw0FpbA/gf8N+sjTJ3Sed7shgIs9Y2An4E3sjaKHOXdL4nGGMKAv8C5mZthLlTet4XY0xN4AngCmttfeDBrI4zN0nn78rTwChrbROgH/BR1kaZK30FdEtjf3egpu82BPg4C2IClKSdT3NgvbV2o7X2JPAD0POsY3oCX/vu/wh0NsaYLIwxtznve2KtnWKtPe57OAeokMUx5jbp+T0BeAn3ISY2K4PLxdLzvtwBfGitPQhgrd2TxTHmNul5TyxQyHe/MLAjC+PLlay104EDaRzSE/jGOnOAIsaYslkRm5K0tJUHtqV4HO3bluox1toE4DBQPEuiy53S856kdBvwR6ZGJOd9T3zdAxWttb9lZWC5XHp+V2oBtYwxM40xc4wxabUmyKVLz3vyPDDAGBMN/A7cnzWhSRou9P9OhgnKiouIeMEYMwAIA9p7HUtuZowJAN4BBnkcivxTEK4LpwOuxXm6MaahtfaQl0Hlcv2Br6y1bxtjWgHfGmMaWGuTvA5Msp5a0tK2HaiY4nEF37ZUjzHGBOGap/dnSXS5U3reE4wx4cBTwDXW2rgsii23Ot97UhBoAEw1xmwGWgLjNXkg06XndyUaGG+tjbfWbgLW4pI2yRzpeU9uA0YBWGtnAyG49SPFO+n6v5MZlKSlbT5Q0xhT1RiTBzeIc/xZx4wHBvru9wUmWxWfy0znfU+MMU2AT3EJmsbYZL403xNr7WFrbQlrbRVrbRXcOMFrrLULvAk310jP369xuFY0jDElcN2fG7MwxtwmPe/JVqAzgDGmLi5J25ulUcrZxgO3+GZ5tgQOW2t3ZsWF1d2ZBmttgjHmPuBPIBAYZq1daYx5EVhgrR0PfIFrjl6PG3jYz7uIc750vidvAgWA0b45HFuttdd4FnQOl873RLJYOt+XP4EuxphVQCLwiLVWPQGZJJ3vyX+Az4wx/8ZNIhikD/6ZyxgzAvdhpYRvLOBzQDCAtfYT3NjAK4H1wHFgcJbFpvdeRERExP+ou1NERETEDylJExEREfFDStJERERE/JCSNBERERE/pCRNRERExA8pSRORdDHGlDTGzDDGrDDG9Eqx/WdjTLmLONdcY8xiY0zbFNt/MsYsMcasN8Yc9t1fYoxpfZExVzHGnEhxniXGmFt8+zb7aoNd6DkHpff1GmO6G2MWGGNW+V7r2xd6Pd95nryY54lI9qY6aSKSXv2BT4CxuLpB44wxPYDF1toLXQS6M7DcWnt7yo3W2t4AxpgOwMPW2qsvNWhgg7W2cQac55RBwArOs/C1MaYB8AFwlbX2b2NMIDDkIq/5JPDqRT73ghljgnxrEYuIh9SSJiLpFQ/kB/ICib5l0B4E3jjXE3wtWZONMcuMMZOMMZWMMY19z+npa9nKl9ZFUzuHb/tXxphPfC1Va40xF53QGWPGGWMWGmNWGmOG+LYF+q6xwhiz3Bjzb2NMX9x6sN+lI/ZHgVestX8DWGsTrbUfp4i9b4rrx/i+ljXGTPede4Uxpq0x5nUgn2/bd77jHvLtX2GMeTDF9+lv37nXGmO+M8aEG7d4+jpjTHPfcaHGmGHGmHm+1r2evu2DjDHjjTGTgUmpxXKx318RuUjWWt100023895w69L+BizAtYQ9gKuGntZzfgEG+u7fCozz3R8EfJDG8zoAv57nHF8BE3AfNmvi1qEMOes8VYATwJIUt7a+fZuBEr77xXxf8+FayYoDTYHIFOcq4vs6FQhLx/drEXDZOfZ9BfRN8TjG9/U/wFO++4FAwZT7ffebAsuBUNzKGiuBJr7XmgA09H1PFgLDAAP0TPF9exUYcOo14dbrDPW9J9EpvhepxqKbbrpl3U3dnSKSLtbaw8BVAMaYosDjQG9jzGdAUeBt6xaETqkV0Md3/1vSaHVLQ1rnGGWtTQLWGWM2AnVwiVhK6enufMAY09t3vyIu6VsDVDPGvI9LTideROwXaj4wzBgTjEuqlqRyTBvgJ2vtMQBjzFigLW59wU3W2uW+7SuBSdZaa4xZjkviALoA1xhjHvY9DgEq+e5HWmsPXEAsIpKJ1N0pIhfjGeAV3Di1GcBA4HkP4jh7XbsLXufON/4tHGhlrb0MWIxrkTsIXIZrObsL+PwCT70S1+qVmgR8f3+NMQFAHgBr7XSgHbAd+OrUJIcLEJfiflKKx0mcHoNsgGuttY19t0rW2tW+fcdOPTkDYhGRS6QkTUQuiDGmJlDBWjsVN0YtCZccpTY+axbQz3f/JuCvi7hkWue4zhgTYIypDlTDtX5dqMLAQWvtcWNMHaAlgG/mZ4C1dgzwNHC57/ijQMF0nPdN4EljTC3f+QKMMXf59m3mdAJ3Db7FnI0xlYHd1trPcEnhqWvG+1q0wL3+XsaY/MaYUKA3F/Z9/RO43xhjfNdsktpBacQiIllE3Z0icqFeAZ7y3R8BjMN1fT6byrH3A18aYx4B9gKDL+J6aZ1jKzAPKATcZa2NTeX51Y0xS1I8HmatfS/F4wnAXcaY1bgkb45ve3nfdU99mH3C9/Ur4BNjzAlcV+wTwAJr7fiUF7XWLvMN6h9hjMmPS2R/9e3+DPjZGLPUd/1TLVgdgEeMMfFADHCq9WoosMwYs8hae5Mx5ivf6wb43Fq72BhTJZXXnpqXgP/znS8A2ASkNuniXLGISBYx1l5w74CIiOd8icqv1tofvY5FRCQzqLtTRERExA+pJU1ERETED6klTURERMQPKUkTERER8UNK0kRERET8kJI0ERERET+kJE1ERETEDylJExEREfFD/w+yKOtAsqH1gAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "cumm_gain = cumulative_gain_ci(debiased_test_pred, \"m3_pred\", \"sales\", \"price\", min_periods=50, steps=200)\n",
    "x = np.array(range(len(cumm_gain)))\n",
    "plt.plot(x/x.max(), cumm_gain, color=\"C1\")\n",
    "\n",
    "plt.plot([0, 1], [0, elast(debiased_test_pred, \"sales\", \"price\")], linestyle=\"--\", label=\"Random Model\", color=\"black\")\n",
    "\n",
    "plt.xlabel(\"% of Top Elast. Customers\")\n",
    "plt.title(\"Cumulative Gains on Biased Sample\")\n",
    "plt.ylabel(\"Cumulative Gains\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First thing you should notice is that the average elasticity goes up, instead of down. We've seen this before. In the biased data, it looks like sales goes up as price increases. As a result, the final point in the cumulative gain plot is positive. This makes little sense, since we now people don't buy more as we increase ice cream prices. If the average price elasticity is already messed up, any ordering in it also makes little sense. The bottom line being that this data should not be used for model evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orthogonalization with Machine Learning\n",
    "\n",
    "In a 2016 paper, Victor Chernozhukov *et all* showed that you can also do orthogonalization with machine learning models. This is obviously very recent science and we still have much to discover on what we can and can't do with ML models. Still, it's a very interesting idea to know about.\n",
    "\n",
    "The nuts and bolts are pretty much the same to what we've already covered. The only difference is that now, we use machine learning models for the debiasing. \n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\hat{y}_i &= y_i - M_y(X_i) \\\\\n",
    "\\hat{t}_i &= t_i - M_t(X_i)\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "There is a catch, though. As we know very well, machine learning models are so powerful that they can fit the data perfectly, or rather, overfit. Just by looking at the equations above, we can know what will happen in that case. If $M_y$ somehow overfits, the residuals will all be very close to zero. If that happens, it will be hard to find how $t$ affects it. Similarly, if $M_t$ somehow overfits, its residuals will also be close to zero. Hence, there won't be variation in the treatment residual to see how it can impact the outcome. \n",
    "\n",
    "To account for that, we need to do sample splitting. That is, we estimate the model with one part of the dataset and we make predictions in the other part. The simplest way to do this is to split the test sample in half, make two models  in such a way that each one is estimated in one half of the dataset and makes predictions in the other half. \n",
    "\n",
    "A slightly more elegant implementation uses K-fold cross validation. The advantage being that we can train all the models on a sample which is bigger than half the test set.\n",
    "\n",
    "![img](./data/img/orthogonal/kfold-cv.png)\n",
    "\n",
    "Fortunately, this sort of cross prediction is very easy to implement using Sklearn's `cross_val_predict` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "X = [\"cost\", \"weekday\", \"temp\"]\n",
    "t = \"price\"\n",
    "y = \"sales\"\n",
    "\n",
    "folds = 5\n",
    "\n",
    "np.random.seed(123)\n",
    "m_t = RandomForestRegressor(n_estimators=100)\n",
    "t_res = test[t] - cross_val_predict(m_t, test[X], test[t], cv=folds)\n",
    "\n",
    "m_y = RandomForestRegressor(n_estimators=100)\n",
    "y_res = test[y] - cross_val_predict(m_y, test[X], test[y], cv=folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the residuals, let's store them as columns on a new dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>weekday</th>\n",
       "      <th>cost</th>\n",
       "      <th>price</th>\n",
       "      <th>sales</th>\n",
       "      <th>sales-ML_y(X)</th>\n",
       "      <th>price-ML_t(X)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7791</th>\n",
       "      <td>20.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>187</td>\n",
       "      <td>-3.150833</td>\n",
       "      <td>-0.869267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>26.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>201</td>\n",
       "      <td>-0.418857</td>\n",
       "      <td>-0.192867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5785</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>186</td>\n",
       "      <td>-2.515667</td>\n",
       "      <td>0.790429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542</th>\n",
       "      <td>20.9</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>180</td>\n",
       "      <td>-11.718500</td>\n",
       "      <td>-1.280460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9250</th>\n",
       "      <td>26.7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>201</td>\n",
       "      <td>-1.214167</td>\n",
       "      <td>1.715117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      temp  weekday  cost  price  sales  sales-ML_y(X)  price-ML_t(X)\n",
       "7791  20.8        3   1.5    6.3    187      -3.150833      -0.869267\n",
       "1764  26.6        3   1.5    6.3    201      -0.418857      -0.192867\n",
       "5785  24.0        4   1.0    5.8    186      -2.515667       0.790429\n",
       "3542  20.9        3   1.5    5.1    180     -11.718500      -1.280460\n",
       "9250  26.7        5   1.0    7.0    201      -1.214167       1.715117"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_debiased_test = test.assign(**{\n",
    "    \"sales-ML_y(X)\": y_res,\n",
    "    \"price-ML_t(X)\": t_res,\n",
    "})\n",
    "ml_debiased_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can plot the debiased dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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   "source": [
    "np.random.seed(123)\n",
    "sns.scatterplot(data=ml_debiased_test.sample(1000),\n",
    "                x=\"price-ML_t(X)\", y=\"sales-ML_y(X)\", hue=\"weekday\");"
   ]
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    "Once again, we've uncovered a negative price elasticity on sales. Actually, the plot is incredibly similar to the one we've got when using simple linear regression. But that's probably because this is a very simple dataset. The advantages of machine learning orthogonalization is that it can estimate more complicated functions. It can learn interactions and non linearities in a way that it's hard to encode into linear regression. Also, there is the advantage that some machine learning models (those bases on decision trees) are much simpler to run than linear regression. They can handle categorical data, outliers and even missing data, stuff that would require some attention if you are just using linear regression. \n",
    "\n",
    "Finally, before we close, I just need to cover one final common mistake that data scientists often make when they are introduced to this idea (been there, done that). If the treatment or the outcome is binary, one might think it is better to replace the machine learning regression models for their classification versions. However, this does not work. The theory of orthogonalization only functions under regression models, similarly with what we've seen a long time ago when talking about Instrumental Variables. To be honest, it is not that the model will fail miserably if you replace regression by classification, but I would advise against it. If the theory doesn't justify it, why run the risk? \n",
    "\n",
    "## Key Ideas\n",
    "\n",
    "We've started the chapter by highlighting the necessity of random treatment assignment in order for our causal evaluation methods to work. This poses a problem in the case where random data is not available. To be clear, the safest solution in this case is to go and do some experiments in order to get random data. If that is out of the question, only then, we can rely on a clever alternative: transform our data to look as if the treatment has been randomly assigned. \n",
    "\n",
    "Here, we've covered how to do that using the principles of orthogonalization. First, we've built a model that uses our features $X$ to predict the treatment $t$ and get it's residuals. The idea being that the treatment residuals is, by definition, independent of the features used to construct it. In other words, the treatment residuals are orthogonal to the features. We can see these residuals as a version of the treatment where all the confounding bias due to $X$ has been removed. \n",
    "\n",
    "That alone is enough to make our data look as good as random. But we can go one step further. We can build a model that predicts the outcome $y$ using the features $X$ but not the treatment and also get its residuals. Again, the intuition is very similar. These outcome residuals is a version of the outcome where all the variance due to the features has been explained away. That will hopefully explay a lot of the variance, making it easier to see the treatment effect.\n",
    "\n",
    "Here we are using orthogonalization with the goal of debiasing our data for model evaluation. However, this technique is also used for other purposes. Namely, lot's of causal inference models use orthogonalization as a first pre-processing step to ease the task of the causal inference model. We can say that orthogonalization makes the foundation of many modern causal inference algorithms.\n",
    "\n",
    "![img](./data/img/orthogonal/athlas.png)\n",
    "\n",
    "\n",
    "## References \n",
    "\n",
    "The things I've written here are mostly stuff from my head. I've learned them through experience. This means that they have **not** passed the academic scrutiny that good science often goes through. Instead, notice how I'm talking about things that work in practice, but I don't spend too much time explaining why that is the case. It's a sort of science from the streets, if you will. However, I am putting this up for public scrutiny, so, by all means, if you find something preposterous, open an issue and I'll address it to the best of my efforts.\n",
    "\n",
    "This chapter is based on Victor Chernozhukov *et all* (2016), Double/Debiased Machine Learning for Treatment and Causal Parameters. You can also check Frisch, Ragnar; Waugh, Frederick V. (1933) original article, Partial Time Regressions as Compared with Individual Trends. \n",
    "\n",
    "## Contribute\n",
    "\n",
    "Causal Inference for the Brave and True is an open-source material on causal inference, the statistics of science. It uses only free software, based in Python. Its goal is to be accessible monetarily and intellectually.\n",
    "If you found this book valuable and you want to support it, please go to [Patreon](https://www.patreon.com/causal_inference_for_the_brave_and_true). If you are not ready to contribute financially, you can also help by fixing typos, suggesting edits or giving feedback on passages you didn't understand. Just go to the book's repository and [open an issue](https://github.com/matheusfacure/python-causality-handbook/issues). Finally, if you liked this content, please share it with others who might find it useful and give it a [star on GitHub](https://github.com/matheusfacure/python-causality-handbook/stargazers)."
   ]
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